The basic Fundamental to human-centric computer vision is to make the human motion see and understandable by machines. The hectic task is that the video containing enormous amount of information in the form of pixels, much of meaningless to a computer unless it can decode the data within the pixels. To make it possible, computer what is the mechanism behind which pixel go together and what it represents. The process of detecting and tracking the pixels representing the form of humans is to be notified as Human motion capture. Where there is a lacking of count of the people and we want to overcome. We plan to achieve this goal using intermediate level deep learning project on computer vision concepts, where deep learning is an AI method that imitate the functioning of human brain in processing data for use of object detection, speech recognition, translating languages, and making decisions. OpenCV is the place where it deals will all sorts of camera related things and make the detection easier. This work represents that how a human is detected and counted using SVM. The main idea is to detect the patterns of human motion, to a larger extent which is independent of differences in appearance. To do so, an HOG descriptor is used to detect the patterns of the frame captured, the greatest use of this descriptor is that it detects the patterns with the direction of the movement of the captured picture and hence it makes the job easy to train the pictures using the SVM and get the human detected.

Computer Vision; OpenCV; Support Vector Machine; HOG descriptor; Video Surveillance: Human Detection

[1]. W Fernando et al., in Information and Automation for Sustainability (ICIAfS), 2014 7th International Conference on. Object identification, enhancement and tracking under dynamic background conditions, IEEE, 2014
[2]. Xu, R., Guan, Y., & Huang, Y., Multiple human detection and tracking based on head detection for real-time video surveillance. Multimedia Tools and Applications, 74(3), 729-742, 2015.
[3]. Dalal, N., Triggs, B., & Schmid, C., Human detection using oriented histograms of flow and appearance. In European conference on computer vision. Springer, Berlin, Heidelberg. pp. 428-441, May, 2006.
[4]. https://www.researchgate.net/publication/4215591_Real-Time_Human_Detection_Tracking_and_Verification_in_Uncontrolled_Camera_Motion_Environments
[5]. Sulman, N., Sanocki, T., Goldgof, D., & Kasturi, R., How effective is human video surveillance performance? In 2008 19th International Conference on Pattern Recognition, IEEE, pp. 1-3, December, 2008.
[6]. Murat EKINCI, Ey ?up GEDIKL "Silhouette Based Human Motion Detection and Analysis for Real-Time Automated Video Surveillance" Dept. of Computer Engineering, Karadeniz Technical University, Trabzon, TURKEY, 2005.
[7]. Pang, Y., Yuan, Y., Li, X., & Pan, J., Efficient HOG human detection. Signal Processing, 91(4), 773-781, 2011.
[8]. N. Cristianini and J. Shawe-Taylor. Support vector net [http://www.supportvector.net/]. Cambridge University, 2015.
[9]. Osuna, E., Freund, R., & Girosit, F., Training support vector machines: an application to face detection. In Proceedings of IEEE computer society conference on computer vision and pattern recognition. IEEE. pp. 130-136, June, 1997.
[10]. Smail Haritaoglu, David Harwood and Larry S. Davis “W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People" Computer Vision Laboratory, University of Maryland College Park, 1998.
(PDF) Real-Time Human Motion Detection and Tracking. Available from: https://www.researchgate.net/publication/251852856_Real-Time_Human_Motion_Detection_and_Tracking
[11]. Kim, Y., & Ling, H., Human activity classification based on micro-Doppler signatures using a support vector machine. IEEE Transactions on Geoscience and Remote Sensing, 47(5), 1328-1337, 2009.
[12]. Foroughi, H., Rezvanian, A., & Paziraee, A., Robust fall detection using human shape and multi-class support vector machine. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE. pp. 413-420, December, 2008.
[13]. Dadi, H. S., & Pillutla, G. M., Improved face recognition rate using HOG features and SVM classifier. IOSR Journal of Electronics and Communication Engineering, 11(4), 34-44, 2016.
[14]. Chen, P. Y., Huang, C. C., Lien, C. Y., & Tsai, Y. H., An efficient hardware implementation of HOG feature extraction for human detection. IEEE Transactions on Intelligent Transportation Systems, 15(2), 656-662, 2013.
[15]. Liang, Y., Reyes, M. L., & Lee, J. D., Real-time detection of driver cognitive distraction using support vector machines. IEEE transactions on intelligent transportation systems, 8(2), 340-350, 2007.
[16]. Vijayalakshmi, S., & Kumar, N. S., A Review on Fetal Brain Structure Extraction Techniques from Human MRI Images. 2008.
[17]. Vayadande, K. B., & Yadav, S., A Review paper on Detection of Moving Object in Dynamic Background, 2018.