Skip to main content
SpringerLink
Log in

Human detection techniques for real time surveillance: a comprehensive survey

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Real-time detection of humans is an evolutionary research topic. It is an essential and prominent component of various vision based applications. Detection of humans in real-time video sequences is an arduous and challenging task due to various constraints like cluttered environment, occlusion, noise, etc. Many researchers are doing their research in this area and have published the number of researches so far. Determining humans in visual monitoring system is prominent for different types of applications like person detection and identification, fall detection for an elder person, abnormal surveillance, gender classification, crowd analysis, person gait characterization, etc. The main objective of this paper is to provide a comprehensive survey of the various challenges and modern developments seen for human detection methodologies in day vision. This paper consists of an overview of different human detection techniques and their classification based on various underlying factors. The algorithmic technicalities with their applicability to these techniques are deliberated in detail in the manuscript. Different humanitarian imperative factors have also been highlighted for comparative analysis of each human detection methodology. Our survey shows the difference between current research and future requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Abughalieh K, Alawneh S (2020) Pedestrian orientation estimation using cnn and depth camera. Tech. rep., SAE Technical Paper

  2. Ahmed A, Guo J, Ali F, Deeba F, Ahmed A (2018) Lbph based improved face recognition at low resolution. In: 2018 international conference on artificial intelligence and big data (ICAIBD), IEEE, pp 144–147

  3. Aibinu AM, Shafie AA, Salami MJE (2012) Performance analysis of ann based ycbcr skin detection algorithm. Procedia Eng 41:1183–1189

    Google Scholar 

  4. Al-Mohair HK, Saleh J, Saundi S (2013) Impact of color space on human skin color detection using an intelligent system. In: 1st WSEAS international conference on image processing and pattern recognition (IPPR’13), vol 2

  5. Al-Mohair HK, Saleh JM, Suandi SA (2015) Hybrid human skin detection using neural network and k-means clustering technique. Appl Soft Comput 33:337–347

    Google Scholar 

  6. AlDahoul N, Sabri M, Qalid A, Mansoor AM (2018) Real-time human detection for aerial captured video sequences via deep models. Comput Intell Neurosci p 2018

  7. Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: 2008 IEEE conference on computer vision and pattern recognition, IEEE, pp 1–8

  8. Ansari MA, Dixit M (2017a) An image retrieval framework: a review. Int J Adv Res Comput Sci 8(5)

  9. Ansari MA, Dixit M (2017b) A refined approach of image retrieval using rbf-svm classifier. Int J Signal Process Image Process Pattern Recognit 10(9):43–56

    Google Scholar 

  10. Ansari MA, Singh DK (2018) Review of deep learning techniques for object detection and classification. In: International conference on communication, networks and computing. Springer, New York, pp 422–431

  11. Ansari MA, Kurchaniya D, Dixit M (2018) A comprehensive analysis of image edge detection techniques. Int J Multimed Ubiquitous Eng 12:1–12

    Google Scholar 

  12. Astawa INGA, Putra KG, Sudarma M, Hartati RS (2017) The impact of color space and intensity normalization to face detection performance. Telkomnika 15(4):1894–1899

    Google Scholar 

  13. Bajaj K, Singh DK, Ansari MA (2020) Autoencoders based deep learner for image denoising. Procedia Comput Sci 171:1535–1541

    Google Scholar 

  14. Bhoyar K, Kakde O (2010) Skin color detection model using neural networks and its performance evaluation. In: Journal of computer science. Citeseer, New Jersey

  15. Bhuvaneswari K, Rauf HA (2009) Edgelet based human detection and tracking by combined segmentation and soft decision. In: 2009 international conference on control, automation, communication and Energy Conservation, IEEE, pp 1–6

  16. Bianconi F, Bello R, Fernández A, González E (2015) On comparing colour spaces from a performance perspective: application to automated classification of polished natural stones. In: International conference on image analysis and processing. Springer, New York, pp 71–78

  17. Brand J, Mason JS (2000) A comparative assessment of three approaches to pixel-level human skin-detection. In: Proceedings 15th international conference on pattern recognition. ICPR-2000, IEEE, vol 1, pp 1056–1059

  18. Brown DA, Craw I, Lewthwaite J (2001) A som based approach to skin detection with application in real time systems. In: BMVC, vol 1. Citeseer, New Jersey, pp 491–500

  19. Burger W, Burge MJ (2016) Colorimetric color spaces. In: Digital image processing. Springer, New York, pp 341–365

  20. Bush IJ, Abiyev R, Ma’aitah MKS, Altıparmak H (2018) Integrated artificial intelligence algorithm for skin detection. In: ITM web of conferences, EDP sciences, vol 16, p 02004

  21. Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vis Comput 18(1):63–75

    Google Scholar 

  22. Chahyati D, Fanany MI, Arymurthy AM (2017) Tracking people by detection using cnn features. Procedia Comput Sci 124:167–172

    Google Scholar 

  23. Chai D, Ngan KN (1999) Face segmentation using skin-color map in videophone applications. IEEE Trans Circ Syst Video Technol 9(4):551–564

    Google Scholar 

  24. Chakraborty B, Rius I, Pedersoli M, Mozerov M, Gonzàlez J (2007) Component-based human detection

  25. Chandrappa D, Ravishankar M, RameshBabu D (2011) Face detection in color images using skin color model algorithm based on skin color information. In: 2011 3rd international conference on electronics computer technology, IEEE, vol 1, pp 254–258

  26. Cheddad A, Condell J, Curran K, Mc Kevitt P (2009a) A new colour space for skin tone detection. In: 2009 16th IEEE international conference on image processing (ICIP), IEEE, pp 497–500

  27. Cheddad A, Condell J, Curran K, Mc Kevitt P (2009b) A skin tone detection algorithm for an adaptive approach to steganography. Signal Process 89 (12):2465–2478

    MATH  Google Scholar 

  28. Chen N, Chen WN, Zhang J (2015) Fast detection of human using differential evolution. Signal Process 110:155–163

    Google Scholar 

  29. Chen Y, Tian Y, He M (2020) Monocular human pose estimation: a survey of deep learning-based methods. Comput Vision Image Understand 192:102–897

    Google Scholar 

  30. Chiachia G, Marana AN, Ruf T, Ernst A (2011) Census histograms: a simple feature extraction and matching approach for face recognition. Int J Pattern Recognit Artif Intell 25(08):1337–1348

    MathSciNet  Google Scholar 

  31. Choudhury SK, Sa PK, Padhy RP, Sharma S, Bakshi S (2018) Improved pedestrian detection using motion segmentation and silhouette orientation. Multimed Tools Appl 77(11):13,075–13,114

    Google Scholar 

  32. Cotrina C, Bazán K, Oblitas J, Avila-George H, Castro W (2018) Using machine learning techniques and different color spaces for the classification of cape gooseberry (physalis peruviana l.) fruits according to ripeness level. PeerJ PrePrints

  33. Cuimei L, Zhiliang Q, Nan J, Jianhua W (2017) Human face detection algorithm via haar cascade classifier combined with three additional classifiers. In: 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI), IEEE, pp 483–487

  34. Dai Y, Nakano Y (1996) Face-texture model based on sgld and its application in face detection in a color scene. Pattern Recognit 29(6):1007–1017

    Google Scholar 

  35. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), IEEE, vol 1, pp 886–893

  36. Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: European conference on computer vision. Springer, New York, pp 428–441

  37. Dargan S, Kumar M, Ayyagari MR, Kumar G (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Archiv Comput Meth Eng pp 1–22

  38. Das D, Saharia D, et al. (2014) Implementation and performance evaluation of background subtraction algorithms. arXiv:14051815

  39. De Souza F, Pedrini H (2017) Detection of violent events in video sequences based on census transform histogram. In: 2017 30th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), IEEE, pp 323–329

  40. Deng Y, Luo P, Loy CC, Tang X (2014) Pedestrian attribute recognition at far distance. In: Proceedings of the 22nd ACM international conference on multimedia, pp 789–792

  41. Dollar P, Wojek C, Schiele B, Perona P (2011) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761

    Google Scholar 

  42. Dong L, Dong W, Feng N, Mao M, Chen L, Kong G (2017) Color space quantization-based clustering for image retrieval. Front Comput Sci 11 (6):1023–1035

    Google Scholar 

  43. Dow CR, Ngo HH, Lee LH, Lai PY, Wang KC, Bui VT (2020) A crosswalk pedestrian recognition system by using deep learning and zebra-crossing recognition techniques. Softw Pract Exp 50(5): 630–644

    Google Scholar 

  44. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New York

    Google Scholar 

  45. Dwivedi N, Singh DK, Kushwaha DS (2020) Orientation invariant skeleton feature (oisf): a new feature for human activity recognition. Multimed Tools Appl

  46. El-dosuky MA, Oliva D, Hassanien AE (2020) An artificial intelligence system for apple fruit disease classification based on support vector machine and cockroach swarm optimization. In: Joint european-US workshop on applications of invariance in computer vision. Springer, New York, pp 137–147

  47. Elgammal A, Muang C, Hu D (2009) Skin detection-a short tutorial. Encycloped Biomet 4:1218–1224

    Google Scholar 

  48. Endah SN, Kusumaningrum R, Wibawa HA (2017) Color space to detect skin image: the procedure and implication. Scientif J Inform 4(2):143–149

    Google Scholar 

  49. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vision 111(1):98–136

    Google Scholar 

  50. Feraund R, Bernier OJ, Viallet JE, Collobert M (2001) A fast and accurate face detector based on neural networks. IEEE Trans Pattern Anal Mach Intell 23(1):42–53

    Google Scholar 

  51. Ferryman J, Shahrokni A (2009) Pets2009: dataset and challenge. In: 2009 twelfth IEEE international workshop on performance evaluation of tracking and surveillance, IEEE, pp 1–6

  52. Firoze A, Deb T (2018) Face recognition time reduction based on partitioned faces without compromising accuracy and a review of state-of-the-art face recognition approaches. In: Proceedings of the 2018 international conference on image and graphics processing, pp 14–21

  53. Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, New York, pp 23–37

  54. Gaikwad V, Lokhande S (2015) Vision based pedestrian detection for advanced driver assistance. Procedia Comput Sci 46:321–328

    Google Scholar 

  55. Gajjar V, Gurnani A, Khandhediya Y (2017) Human detection and tracking for video surveillance: a cognitive science approach. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2805–2809

  56. Garcia-Garcia B, Bouwmans T, Silva AJR (2020) Background subtraction in real applications: challenges, current models and future directions. Comput Sci Rev 35:100,204

    MathSciNet  MATH  Google Scholar 

  57. Garcia-Martin A, Martínez JM (2012) On collaborative people detection and tracking in complex scenarios. Image Vision Comput 30(4-5):345–354

    Google Scholar 

  58. Ghazali KHB, Ma J, Xiao R, et al. (2012) An innovative face detection based on ycgcr color space. Phys Procedia 25:2116–2124

    Google Scholar 

  59. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  60. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  61. Gomez G, Sanchez M, Sucar LE (2002) On selecting an appropriate colour space for skin detection. In: Mexican international conference on artificial intelligence. Springer, New York, pp 69–78

  62. Gonzales RC, Woods RE (2002) Digital image processing

  63. González A, Fang Z, Socarras Y, Serrat J, Vázquez D, Xu J, López AM (2016) Pedestrian detection at day/night time with visible and fir cameras: a comparison. Sensors 16(6):820

    Google Scholar 

  64. Greche L, Es-Sbai N (2016) Automatic system for facial expression recognition based histogram of oriented gradient and normalized cross correlation. In: 2016 international conference on information technology for organizations development (IT4OD), IEEE, pp 1–5

  65. Guo J, Wang J, Bai R, Zhang Y, Li Y (2017) A new moving object detection method based on frame-difference and background subtraction. In: IOP conference series: materials science and engineering, vol 242. IOP Publishing, Bristol, p 012115

  66. Haiyuan W, Chen Q, Yachida M (1999) Face detection from color images using a fuzzy pattern matching method. IEEE Trans Pattern Anal Mach Intell 21 (6):557–563. https://doi.org/10.1109/34.771326

    Article  Google Scholar 

  67. Han H, Tong M (2013) Human detection based on optical flow and spare geometric flow. In: 2013 seventh international conference on image and graphics, IEEE, pp 459–464

  68. Han H, Tong M (2013) Human detection based on optical flow and spare geometric flow. In: 2013 seventh international conference on image and graphics, pp 459–464

  69. Hapsari G, Prabuwono AS (2010) Human motion recognition in real-time surveillance system: a review. J Appl Sci (Faisalabad) 10(22):2793–2798

    Google Scholar 

  70. Hassanpour R, Shahbahrami A, Wong S (2008) Adaptive gaussian mixture model for skin color segmentation. World Academy Sci Eng Technol 41:1–6

    Google Scholar 

  71. He K, Gkioxari G, Dollár P, Girshick R (2017a) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

  72. He M, Luo H, Chang Z, Hui B (2017b) Pedestrian detection with semantic regions of interest. Sensors 17(11):2699

    Google Scholar 

  73. Hoang VD, Hernandez DC, Jo KH (2014) Partially obscured human detection based on component detectors using multiple feature descriptors. In: International conference on intelligent computing. Springer, New York, pp 338–344

  74. Horprasert T, Harwood D, Davis LS (1999) A statistical approach for real-time robust background subtraction and shadow detection. In: Ieee iccv, vol 99. Citeseer, New Jersey, pp 1–19

  75. Hsu FC, Gubbi J, Palaniswami M (2013) Human head detection using histograms of oriented optical flow in low quality videos with occlusion. In: 2013, 7th international conference on signal processing and communication systems (ICSPCS), IEEE, pp 1–6

  76. Jedynak B, Zheng H, Daoudi M (2003) Statistical models for skin detection. In: 2003 conference on computer vision and pattern recognition workshop, IEEE, vol 8, pp 92–92

  77. Jones MJ, Rehg JM (2002) Statistical color models with application to skin detection. Int J Comput Vis 46(1):81–96

    MATH  Google Scholar 

  78. Kahu SY, Raut RB, Bhurchandi KM (2019) Review and evaluation of color spaces for image/video compression. Color Res Appl 44(1):8–33

    Google Scholar 

  79. Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin-color modeling and detection methods. Pattern Recognit 40(3):1106–1122

    MATH  Google Scholar 

  80. Kale K, Pawar S, Dhulekar P (2015) Moving object tracking using optical flow and motion vector estimation. In: 2015 4th international conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions), IEEE, pp 1–6

  81. Kalwa U, Legner C, Kong T, Pandey S (2019) Skin cancer diagnostics with an all-inclusive smartphone application. Symmetry 11(6):790

    Google Scholar 

  82. Khalifa AF, Badr E, Elmahdy HN (2019) A survey on human detection surveillance systems for raspberry pi. Image Vis Comput 85:1–13

    Google Scholar 

  83. Khan MA, Javed K, Khan SA, Saba T, Habib U, Khan JA, Abbasi AA (2020) Human action recognition using fusion of multiview and deep features: an application to video surveillance. Multimed Tools Appl pp 1–27

  84. Khan R, Hanbury A, Stöttinger J, Bais A (2012) Color based skin classification. Pattern Recogn Lett 33(2):157–163

    Google Scholar 

  85. Khelalef A, Ababsa F, Benoudjit N (2019) An efficient human activity recognition technique based on deep learning. Pattern Recognit Image Anal 29 (4):702–715

    Google Scholar 

  86. Kim B, Yuvaraj N, Sri Preethaa K, Santhosh R, Sabari A (2020) Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance. Soft Comput pp 1–12

  87. Kim HK, Park JH, Jung HY (2018) An efficient color space for deep-learning based traffic light recognition. J Adv Transp p 2018

  88. Kumar SH, Sivaprakash P (2013) New approach for action recognition using motion based features. In: 2013 IEEE conference on information & communication technologies, IEEE, pp 1247–1252

  89. Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim KJ (2019) A survey of deep learning-based network anomaly detection. Clust Comput pp 1–13

  90. Ladjailia A, Bouchrika I, Merouani HF, Harrati N, Mahfouf Z (2019) Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput Applic pp 1–14

  91. Lee JY, Yoo SI (2002) An elliptical boundary model for skin color detection. In: Proceedings of the 2002 international conference on imaging science, systems, and technology

  92. Li H, Lin K, Bai J, Li A, Yu J (2019) Small object detection algorithm based on feature pyramid-enhanced fusion ssd. Complexity p 2019

  93. Li Z, Peng C, Yu G, Zhang X, Deng Y, Sun J (2017) Light-head r-cnn: In defense of two-stage object detector. arXiv:171107264

  94. Liu CL, Lee CH, Lin PM (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37(10):7174–7181

    Google Scholar 

  95. Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vision 128(2):261–318

    Google Scholar 

  96. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, New York, pp 21–37

  97. Loesdau M, Chabrier S, Gabillon A (2017) Chromatic indices in the normalized rgb color space. In: 2017 International conference on digital image computing: techniques and applications (DICTA), IEEE, pp 1–8

  98. Lumini A, Nanni L (2018) Fair comparison of skin detection approaches on publicly available datasets. arXiv:180202531

  99. Luo R (2016) Encyclopedia of color science and technology. Springer Publishing Company, Incorporated

  100. Luo X, Guan Q, Tan H, Gao L, Wang Z, Luo X (2017) Simultaneous indoor tracking and activity recognition using pyroelectric infrared sensors. Sensors 17(8):1738

    Google Scholar 

  101. Ma M (2020) Infrared pedestrian detection algorithm based on multimedia image recombination and matrix restoration. Multimed Tools Appl 79(13):9267–9282

    Google Scholar 

  102. Maheswari S, Korah R (2017) Enhanced skin tone detection using heuristic thresholding

  103. Mahmoodi MR (2017) Fast and efficient skin detection for facial detection. arXiv:170105595

  104. Mateus A, Ribeiro D, Miraldo P, Nascimento JC (2019) Efficient and robust pedestrian detection using deep learning for human-aware navigation. Robot Auton Syst 113:23–37

    Google Scholar 

  105. Matsumura R, Hanazawa A (2019) Human detection using color contrast-based histograms of oriented gradients

  106. Mehta R, Ozturk C (2018) Object detection at 200 frames per second. In: Proceedings of the european conference on computer vision (ECCV), pp 0–0

  107. Mohan A, Papageorgiou C, Poggio T (2001) Example-based object detection in images by components. IEEE Trans Pattern Anal Mach Intell 23(4):349–361

    Google Scholar 

  108. Montufar-Chaveznava R (2006) Face tracking using a polling strategy. Proc World Acad Scien Enginner Techn 18:161–165

    Google Scholar 

  109. Mu Y, Yan S, Liu Y, Huang T, Zhou B (2008) Discriminative local binary patterns for human detection in personal album. In: 2008 IEEE conference on computer vision and pattern recognition, IEEE, pp 1–8

  110. Mulyanto A, Borman RI, Prasetyawan P, Jatmiko W, Mursanto P (2019) Real-time human detection and tracking using two sequential frames for advanced driver assistance system. In: 2019 3rd international conference on informatics and computational sciences (ICICoS), pp 1–5

  111. Munder S, Gavrila DM (2006) An experimental study on pedestrian classification. IEEE Trans Pattern Anal Mach Intell 28(11):1863–1868

    Google Scholar 

  112. Nambiar A, Taiana M, Figueira D, Nascimento JC, Bernardino A (2014) A multi-camera video dataset for research on high-definition surveillance. Int J Mach Intell Sens Signal Process 1(3):267–286

    Google Scholar 

  113. Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recogn 51:148–175

    Google Scholar 

  114. Ogale NA (2006) A survey of techniques for human detection from video. Survey Univ Maryland 125(133):19

    Google Scholar 

  115. Ojha U, Adhikari U, Singh DK (2017) Image annotation using deep learning: A review. In: 2017 international conference on intelligent computing and control (I2C2), IEEE, pp 1–5

  116. Ouyang W, Wang X (2012) A discriminative deep model for pedestrian detection with occlusion handling. In: 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp 3258–3265

  117. Pang Y, Yuan Y, Li X, Pan J (2011) Efficient hog human detection. Signal Process 91(4):773–781

    MATH  Google Scholar 

  118. Papageorgiou CP, Oren M, Poggio T (1998) A general framework for object detection. In: Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), IEEE, pp 555–562

  119. Paul M, Haque SM, Chakraborty S (2013) Human detection in surveillance videos and its applications-a review. EURASIP J Adv Signal Process 2013(1):176

    Google Scholar 

  120. Petrović N, Jovanov L, Pižurica A, Philips W (2008) Object tracking using naive bayesian classifiers. In: International conference on advanced concepts for intelligent vision systems. Springer, New York, pp 775–784

  121. Peyré G, Mallat S (2004) Second generation bandelets and their application to image and 3d meshes compression. Math Image Anal MIA p 4

  122. Phung SL, Chai D, Bouzerdoum A (2001) A universal and robust human skin color model using neural networks. In: IJCNN’01. international joint conference on neural networks. proceedings (Cat. No. 01CH37222), IEEE, vol 4, pp 2844–2849

  123. Phung SL, Bouzerdoum A, Chai D, Watson A (2004) Naive bayes face-nonface classifier: a study of preprocessing and feature extraction techniques. In: 2004 international conference on image processing, 2004. ICIP’04., IEEE, vol 2, pp 1385–1388

  124. Piccardi M (2004) Background subtraction techniques: a review. In: 2004 IEEE international conference on systems, man and cybernetics (IEEE cat. no. 04CH37583), IEEE, vol 4, pp 3099–3104

  125. Popov A, Dimitrova D (2008) A new approach for finding face features in color images. In: 2008 4th international IEEE conference intelligent systems, IEEE, vol 2, pp 12–33

  126. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar S (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surveys (CSUR) 51(5):1– 36

    Google Scholar 

  127. Rahimzadeganasl A, Sertel E (2017) Automatic building detection based on cie luv color space using very high resolution pleiades images. In: 2017 25th signal processing and communications applications conference (SIU), IEEE, pp 1–4

  128. Reddy RVK, Raju KP, Kumar LR, Kumar MJ (2016) Grey level to rgb using ycbcr color space technique. Int J Comput Appl 147(7)

  129. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271

  130. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv:180402767

  131. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition , pp 779–788

  132. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  133. Riaz I, Piao J, Shin H (2013) Human detection by using centrist features for thermal images. In: International conference computer graphics visualization computer vision and image processing. Citeseer, New Jersey

  134. Rusia MK, Singh DK, Ansari MA (2019) Human face identification using lbp and haar-like features for real time attendance monitoring. In: 2019 fifth international conference on image information processing (ICIIP), IEEE, pp 612–616

  135. Sayed U, Mofaddel MA, Bakheet S, El-Zohry Z (2018) An elliptical boundary skin model for hand detection based on hsv color space

  136. Schmidt A, Kasiński A (2007) The performance of the haar cascade classifiers applied to the face and eyes detection. In: Computer recognition systems, vol 2. Springer, New York, pp 816–823

  137. Schneider N, Gavrila DM (2013) Pedestrian path prediction with recursive bayesian filters: a comparative study. In: German conference on pattern recognition. Springer, New York, pp 174–183

  138. Schwerdt K, Crowley JL (2000) Robust face tracking using color. In: Proceedings fourth IEEE international conference on automatic face and gesture recognition (Cat. No. PR00580), IEEE, pp 90–95

  139. Sebe N, Cohen I, Huang TS, Gevers T (2004) Skin detection: a bayesian network approach. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004., IEEE, vol 2, pp 903–906

  140. Seemanthini K, Manjunath S (2018) Human detection and tracking using hog for action recognition. Procedia Comput Sci 132:1317–1326

    Google Scholar 

  141. Senst T, Evangelio RH, Sikora T (2011) Detecting people carrying objects based on an optical flow motion model. In: 2011 IEEE workshop on applications of computer vision (WACV), IEEE , pp 301–306

  142. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:13126229

  143. Setjo CH, Achmad B, et al. (2017) Thermal image human detection using haar-cascade classifier. In: 2017 7th international annual engineering seminar (InAES), IEEE, pp 1–6

  144. Shaik KB, Ganesan P, Kalist V, Sathish B, Jenitha JMM (2015) Comparative study of skin color detection and segmentation in hsv and ycbcr color space. Procedia Comput Sci 57(12):41–48

    Google Scholar 

  145. Sharma SK, Agrawal R, Srivastava S, Singh DK (2017) Review of human detection techniques in night vision. In: 2017 international conference on wireless communications, signal processing and networking (WiSPNET), IEEE, pp 2216–2220

  146. Singh DK (2015) Recognizing hand gestures for human computer interaction. In: 2015 international conference on communications and signal processing (ICCSP), IEEE, pp 0379–0382

  147. Singh DK (2017) Gaussian elliptical fitting based skin color modeling for human detection. In: 2017 IEEE 8th control and system graduate research colloquium (ICSGRC), IEEE, pp 197–201

  148. Singh DK (2018) Human action recognition in video. In: International conference on advanced informatics for computing research. Springer, New York, pp 54–66

  149. Singh DK, Kushwaha DS (2016a) Analysis of face feature based human detection techniques. Int J Control Theory Appl 9(22):173–180

    Google Scholar 

  150. Singh DK, Kushwaha DS (2016b) Ilut based skin colour modelling for human detection. Indian J Sci Technol 9:32

    Google Scholar 

  151. Singh DK, Kushwaha DS (2017) Automatic intruder combat system: a way to smart border surveillance. Def Sci J 67(1):50

    Google Scholar 

  152. Sreenu G, Durai MS (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J Big Data 6(1):48

    Google Scholar 

  153. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings 1999 IEEE computer society conference on computer vision and pattern recognition (cat. no PR00149), IEEE, vol 2, pp 246–252

  154. Störring M, Kočka T, Andersen HJ, Granum E (2003) Tracking regions of human skin through illumination changes. Pattern Recognit Lett 24(11):1715–1723

    Google Scholar 

  155. Subban R, Mishra R (2013) Combining color spaces for human skin detection in color images using skin cluster classifier. In: International conference on advances in recent technologies in electrical and electronics. Citeseer, New Jersey, pp 68–73

  156. Sultana F, Sufian A, Dutta P (2020) A review of object detection models based on convolutional neural network. In: Intelligent computing: image processing based applications. Springer, New York, pp 1–16

  157. Sun D, Roth S, Lewis J, Black MJ (2008) Learning optical flow. In: European conference on computer vision. Springer, New York, pp 83–97

  158. Tamgade SN, Bora VR (2009) Motion vector estimation of video image by pyramidal implementation of lucas kanade optical flow. In: 2009 second international conference on emerging trends in engineering & technology, IEEE, pp 914–917

  159. Tang J, Deng C, Huang GB (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821

    MathSciNet  Google Scholar 

  160. Tang S, Goto S (2009) Human detection using motion and appearance based feature. In: 2009 7th international conference on information, communications and signal processing, (ICICS), IEEE, pp 1–4

  161. Teixeira T, Dublon G, Savvides A (2010) A survey of human-sensing: methods for detecting presence, count, location, track, and identity. ACM Comput Surv 5 (1):59–69

    Google Scholar 

  162. Terrillon JC, David M, Akamatsu S (1998) Automatic detection of human faces in natural scene images by use of a skin color model and of invariant moments. In: Proceedings third IEEE international conference on automatic face and gesture recognition, IEEE, pp 112–117

  163. Terrillon JC, Shirazi MN, Fukamachi H, Akamatsu S (2000) Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proceedings fourth IEEE international conference on automatic face and gesture recognition (Cat. No. PR00580), IEEE, pp 54–61

  164. Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vision 104(2):154–171

    Google Scholar 

  165. Vezhnevets V, Sazonov V, Andreeva A (2003) A survey on pixel-based skin color detection techniques. In: Proceedings graphicon, Moscow, Russia, vol 3, pp 85–92

  166. Viola P, Jones M, et al. (2001) Robust real-time object detection. Int J Comput Vision 4(34-47):4

    Google Scholar 

  167. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci p 2018

  168. Walia GS, Kapoor R (2014) Human detection in video and images—a state-of-the-art survey. Int J Pattern Recognit Artif Intell 28(03):1455,004

    Google Scholar 

  169. Wang L, Shi J, Song G, Shen IF (2007) Object detection combining recognition and segmentation. In: Asian conference on computer vision. Springer, New York, pp 189–199

  170. Wang X, Han TX, Yan S (2009) An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th international conference on computer vision, IEEE, pp 32–39

  171. Wang X, Shen C, Li H, Xu S (2019) Human detection aided by deeply learned semantic masks. IEEE Trans Circ Syst Video Technol

  172. Wang Y, Yuan B (2001) A novel approach for human face detection from color images under complex background. Pattern Recogn 34(10):1983–1992

    MATH  Google Scholar 

  173. Womg A, Shafiee MJ, Li F, Chwyl B (2018) Tiny ssd: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection. In: 2018 15th conference on computer and robot vision (CRV), IEEE, pp 95–101

  174. Wong WK, Hui JH, Loo CK, Lim WS (2011) Off-time swimming pool surveillance using thermal imaging system. In: 2011 IEEE international conference on signal and image processing applications (ICSIPA) IEEE, pp 366–371

  175. Wong WK, Hui JH, Desa JBM, Ishak NINB, Sulaiman AB, Nor YBM (2012) Face detection in thermal imaging using head curve geometry. In: 2012 5th international congress on image and signal processing, IEEE, pp 881–884

  176. Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE international conference on computer vision (ICCV’05) volume 1, IEEE, vol 1, pp 90–97

  177. Wu B, Nevatia R (2007) Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int J Comput Vis 75(2):247–266

    Google Scholar 

  178. Wu J, Geyer C, Rehg JM (2011) Real-time human detection using contour cues. In: 2011 IEEE international conference on robotics and automation, IEEE, pp 860–867

  179. Wu X, Sahoo D, Hoi SC (2020) Recent advances in deep learning for object detection. Neurocomputing

  180. Yamashita A, Ito Y, Kaneko T, Asama H (2011) Human tracking with multiple cameras based on face detection and mean shift. In: 2011 IEEE international conference on robotics and biomimetics, IEEE, pp 1664–1671

  181. Yang B, Lei Y, Yan B (2015) Distributed multi-human location algorithm using naive bayes classifier for a binary pyroelectric infrared sensor tracking system. IEEE Sensors J 16(1):216–223

    Google Scholar 

  182. Yang J, Lu W, Waibel A (1998) Skin-color modeling and adaptation. In: Asian conference on computer vision. Springer, New York, pp 687–694

  183. Yang MH, Ahuja N (1998) Gaussian mixture model for human skin color and its applications in image and video databases. In: Storage and retrieval for image and video databases VII, international society for optics and photonics, vol 3656, pp 458–466

  184. Yuan B, Li S (2017) Extended census transform histogram for land-use scene classification. J Appl Remote Sens 11(2):025,003

    Google Scholar 

  185. Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: European conference on computer vision. Springer, New York, pp 151–158

  186. Zhang B, Horváth I, Molenbroek JF, Snijders C (2010) Using artificial neural networks for human body posture prediction. Int J Ind Ergon 40(4):414–424

    Google Scholar 

  187. Zhang H, Hong X (2019) Recent progresses on object detection: a brief review. Multimed Tools Appl 78(19):27,809–27,847

    Google Scholar 

  188. Zhang H, Zhao D, Gao W, Chen X (2000) Combining skin color model and neural network for rotation invariant face detection. In: International conference on multimodal interfaces. Springer, New York, pp 237–244

  189. Zheng J, Ranjan R, Chen CH, Chen JC, Castillo CD, Chellappa R (2020) An automatic system for unconstrained video-based face recognition. IEEE Trans Biomet Behav Ident Sci 2(3):194–209

    Google Scholar 

  190. Zhu A, Wang T, Qiao T (2019) Multiple human upper bodies detection via candidate-region convolutional neural network. Multimed Tools Appl 78 (12):16,077–16,096

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSED, MNNIT Allahabad, Prayagraj, UP, India

    Mohd. Aquib Ansari & Dushyant Kumar Singh

Authors
  1. Mohd. Aquib Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dushyant Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohd. Aquib Ansari.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ansari, M.A., Singh, D.K. Human detection techniques for real time surveillance: a comprehensive survey. Multimed Tools Appl 80, 8759–8808 (2021). https://doi.org/10.1007/s11042-020-10103-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10103-4

Keywords

Search

Navigation