A Liquidity Analysis System for Large-scale Video Streams in the Oilfield

2.1 Overview

Figure 2 shows the architecture design of LinkStream, which composed of a two-tier structure of Edge Device - Central Server. The edge device refers to the multi-channel cameras used to capture video stream, and the central server is responsible for scheduling hardware and executing algorithms. Because different components play different roles in the system, we will run frame filtering and lite object tracking algorithms on the camera, and run object detection algorithms and cross-camera analysis algorithms on the central server. Cross-camera analysis of large-scale video stream will bring huge computational resource overhead, especially when running deep learning algorithms that take up a lot of GPU and memory. Therefore, we use the spatio-temporal correlation of the object liquidity between cameras to optimize system performance. To obtain liquidity information, it is also necessary to convert the results of object tracking into quantitative information. So we have designed a set of algorithms for counting different objects in oilfield to count the numbers. Finally, the system combines the above information with the actual map to get a series of descriptions about the oilfield’s object liquidity information, which can be used by managers to monitor the status of the oilfield area and also can be used by other advanced applications through API.

Fig. 2.

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2.2 Edge Camera Process

For the edge-central architecture, using the computing power of the edge nodes is important to solve the problem of poor cellular network in oilfield area and limited computing power of the central server. The edge node is responsible for tasks such as video capture, lightweight analysis, and dynamic offloading. The task of executing heavy-weight analysis algorithms and integrating the analysis results will be deployed on the central server.

The cameras in this system includes two parts: fixed position cameras and mobile cameras. They have various differences in computing power, network, and system roles. Fixed cameras are generally placed in important areas of the oilfield (such as entrances and exits, crossroads, etc.). Mobile cameras are mainly used to somewhere hard to cover by fixed cameras, including roads and open areas. Because of the hardware limitations of the fixed camera (generally normal webcam), the algorithms it can run are limited, but can transmit data to the central server via network cable. The mobile camera can use a device with computing power such as a smart phone, and it is only able to transmit data to the central server through cellular or Mesh network.

Based on the above analysis, we deploy a small single-camera object tracking algorithm on the mobile camera, and only send the final processing result to the central server. Besides, we adopts a real-time video analysis method based on edge caching and KCF object tracking algorithm on fixed cameras and central server to strike a balance between computational load and computational accuracy. Its basic structure is shown in Figure 3.

Fig. 3.

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The processing module on the central server is mainly divided into two parts, one is responsible for filtering the key-frames that need to be processed, and another is responsible for running the object detection algorithm on the selected key-frames. The edge nodes located near the fixed camera receive the original video stream information on the one hand, and the key-frames object detection result message from the central server on the other hand. Edge nodes cache key-frames analysis results and run lightweight object tracking algorithms (we use multi-threaded KCF algorithm) on all frames. The pseudo-code of the multi-threaded KCF algorithm is shown in Algorithm 1.

For key-frames selection algorithms running on a central server, in general, an increase in the object detection frame rate will improve the accuracy of the analysis, but it will also increase the computational load and processing time. A naive way is to directly use a fixed frame skipping frequency. Note that for different video streams or even different time periods of the same video stream, the impact of the object detection frame rate on the analysis effect is different. Therefore, we propose a dynamic scheduling method for the detection frame rate to automatically adjust the object detection frame rate of each video stream in the system.

This is an optimization problem, the following constraints need to be satisfied:

Let \(S={s_1, s_2, \dots , s_n}\) be the set of video streams. F is the number of frames of the original video stream, and C is the computing resources. Consider each time slice with a length of \(\Gamma\), \(f^{t}_{i}\) is the frame rate of the tth time slice to the video stream \(s_i\). \(\tau _{det}\) and \(\tau _{track}\) are represent the processing delay of object detection and object tracking tasks. \(\phi ^{t}_{i} = \Psi (s_i, v_i^{t - 1})\) is the function of the relationship between the accuracy with video frame rate and the speed of the object. Then the problem can be formally defined as follow: \(\begin{align*} &\max \limits _{f^{t}_{i}} \frac{1}{n} \sum _{i = 1}^{n} \phi ^t_i (f^{t}_{i}) \\ & \begin{array}{rrll} s.t.& f^{t}_{i} &\ge f_{min}, &i=1,2,\ldots ,n \\ &f^{t}_{i} &\le \frac{1 - F \times \tau _{track}}{\tau _{det} - \tau _{track}} &i=1,2,\ldots ,n \\ &\sum _{i = 1}^{n} f^{t}_{i} &\le C. & \\ \end{array} \end{align*}\)

The first constraint gives a lower bound on the number of detection frame rate to ensure that the tracking results will not be too biased. This problem can be solved by the interior point method.

In addition, we adopt an intelligent frame filtering task on the fixed cameras to avoid executing the object detection algorithm in a still frame to improve system. The frame filtering algorithm is mainly based on difference detection [23]. We will hold a buffered frame and calculate the point of difference with newly frame of the video stream. The newly frame with the point greater than the threshold is marked “new” and be used to update buffered frame, otherwise it is marked “same”. The mark information will be sent to the central server along with the video. The judgment of the point of difference mainly relies on the motion vector and the comparison of inter-frame macro blocks.

2.3 Cross-camera Analysis

The main goal of the cross-camera analysis task is to obtain the movement of the same object between different fixed cameras which has been executed single camera tracking.

The distribution of fixed cameras is discrete: compared with the physical area of the oilfield, the coverage area of fixed cameras only occupies a small part, and it takes longer time to move between cameras. So we will solve this problem through cross-camera object re-identification (ReID) based on the single-camera object tracking results. It should be noted that it is expensive to re-identify large-scale objects in large-scale video stream one by one.

Let’s consider a simplified model. Assuming that object appear and disappear evenly in a single camera, in the past T time interval, the expected number of objects leaving a camera is \(O(T)\), and the expected number of objects appearing in a camera is \(I(T)\). Due to the uniformity, we can simply assume that \(O(T)\) and \(I(T)\) are both linear functions, that is, \(I(T) = k_i T\), \(O(T) = k_o T\). In a scene with n video streams, for an object appearing at a camera at time t, the number of possible matches to be considered is \(n O(t) = n k_o t\), because all possible objects in the past need to be matched. Then for all objects in the past time period T the total number of possible matches is approximately: \(\begin{equation*} n \sum _{t_1}^{t_{I(T)}} n k_o t = n^2 k_o \sum _{t_1}^{t_{I(T)}} t = n^2 k_o \times \frac{k_i T^2}{2}. \end{equation*}\)

So we need design an algorithm to tailor the search space. Some empirical analysis [17] shows that the movement of an object between cameras in a specific area shows spatial and temporal correlation. We design a search space pruning algorithm based on this spatio-temporal correlation to improve system performance. We define \(n(c_i, c_j)\) as the historical frequency of appearance in camera \(c_j\) after leaving camera \(c_i\).

The information about the appearance, movement and departure of objects can be obtained by single-camera object tracking algorithm. Due to the sparsity of camera distribution, when an object leaves a certain camera, it may appear in the camera network next time in tens of minutes. It is unreasonable to find the destination of the object in all of the subsequent frames. Therefore, we use reverse matching to track the trajectory. The system saves the characteristic information of the object leaving one monitoring area in the database. When a new tracking object is detected by a camera, we do ReID task on this object with all of unknown location object in the database. If the ReID match is successful, system will report an inter-camera liquidity event and remove related data from the database. Otherwise, system will report that a new object has appeared.

Doing ReID task in historical objects one by one has a large time overhead, so we use spatio-temporal information to prune the search space. Compared with “whereabouts”, reverse matching in our system pay more attention to “source”. Therefore, we define \(S(c_i, c_j)\) as (1) \(\begin{equation} S(c_i, c_j) = \frac{n(c_i, c_j)}{\sum _s n(c_s, c_j)} . \end{equation}\)

\(S(c_i, c_j)\) represents the probability that the object appearing in the \(c_j\) camera comes from the camera \(c_i\). We will give priority to the ReID matching of the object in the camera with a larger robustness. For each source camera \(c_i\), we will only consider objects whose interval time is \(\lambda _{ij}~\pm ~2\sigma _{ij}\), where \(\lambda _{ij}\) and \(\sigma _{ij}\) are the average and standard deviation of the interval time of all objects from \(c_i\) to \(c_j\).

If there is still no matching result after checking the cameras with 95% of the cumulative probability density, we will execute a complete historical object matching. If this match is still not successful, mark the object as a new object and do subsequent operations.

The pseudocode of the object matching module based on spatio-temporal correlation model is as algorithm 2:

How to get the spatio-temporal correlation information used in this algorithm is another important issue. A one-time solution is to perform offline analysis on the dataset of the real scene to get frequency information. This is an expensive one-time operation. The modeled spatio-temporal correlation data can be directly used in system deployment. Furthermore, during the running of the system, the matching results are also recorded, and the recorded results are regularly added to the model to dynamically optimize the spatio-temporal correlation model.

In the actual system implementation, we also added a series of small tricks to improve system performance. First of all, considering that the oilfield has fences and entrances, we have marked some cameras as “entrance” and the object leaving from the specific direction of the monitoring screen of “entrance” cameras is regarded as leaving the oilfield area. These objects will not be added to the database that needs to be tracked. Secondly, we believe that when an object leaves the monitoring area for an hour, it has already resided in certain areas of the oilfield. Further tracking in these object does not have the meaning. Therefore, cross-camera tracking is only for the object within an hour. In addition, we believe that the spatio-temporal correlation model is class-special. This idea is natural due to the difference in the speed of people walking and vehicles traveling. Therefore, we will establish their own spatio-temporal correlation models for different tracking classes.

2.4 Crowd Counting

In order to improve the robustness of the algorithm, we adopt object tracking framework in single-camera object counting task. For objects with obvious feature, such as people and vehicles, direct use of object tracking algorithms has achieved good performance. However, there are few researches on special object like oil tubing, which have similar shapes and be small. We adopt the idea of “Detection + Tracking” based on multi-object tracking to counting to solve the problems of incomplete detection and repeated detection that counting by one pictures. Figure 4 shows that a large number of oil tubing of various shapes are placed on the open area.

Fig. 4.

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Different from the general multi-target tracking task, the task scene is the camera in motion rather than the object in motion, but considering that the tubing is stationary, in essence, or a static and moving situation, and the camera fixed multi-target tracking task situation is not intrinsically different, so it can still use the multi-target tracking processing method for processing. There are still two problems that need to be dealt with. One is the inevitable jitter of the camera in the shooting process, which makes some objects move out of the image for several frames and then move in again, so that they have different ids in the tracking, resulting in repeated counts. Another problem is that some of the tubing will be missed because of factors such as occlusion and lighting, and the dense target is easy to produce detection box overlap, both of which will cause the detection results to be inaccurate and then shadow tracking and counting.

In the object detection stage, we use yolov5l with a larger model scale as the underlying framework for object detection to obtain a higher receptive field. Subsequently, we retrained the modified model on our oil tubing data set. Because the oil tubing are densely packed and irregular, the object detection results often cause box overlap and loss. Therefore, we use geometric laws to correct the object detection. We assume that the oil tubing is arranged in Columns (this assumption is true for the stored oil tubing), so the center point of the detection frame must also be distributed in the vicinity of several straight lines. Based on this assumption, the algorithm can be given as follows:

In addition to geometric correction algorithm, we adds a series of box filter tricks to improve counting accuracy. First of all, in order to ensure the validity of the above correction algorithm, we only counts the oil tubing detected at the position of the center with a left-right ratio of 0.35 to 0.65 and an upper-lower ratio of 0.1 to 0.9. Second, as shown in Figure 5, sometimes distant tubing that are not belong to the current stack will be detected. And this will cause the current tubing stack to count too much. So we correct the result according to the currently detected size of the tubing stack. When there is a detection box with an area smaller than one third of the maximum area detection result, the algorithm will filter it out. The pseudocode is shown in Algorithm 4.

Fig. 5.

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Figure 6 qualitatively shows the detailed steps of our tubing counting process. The original video gets the detection boxes through the object detection algorithm, and the processed image is shown in Figure 6(a). Subsequently, our filter algorithm will only consider object detection boxes within the region shown in Figure 6(b). Finally, our geometric correction algorithm will correct the filtered detection boxes and add an additional detection box in the above example, the result is shown in Figure 6(c). The gray detection box we circled with a blue ellipse in Figure 6(c) is the effect of our algorithm.

Fig. 6.

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After correcting the detection frame, the detection result will be entered into DeepSORT for object tracking, and the unique track-id will be used as the counting object.

2.5 Liquidity Information

In Sections 2.2–2.4, we showed several parts that make up the system. These algorithms finally get some information by analyzing the data of multiple video streams, and we can get liquidity information in this region.

First, we divide the oilfield area into multiple sub-areas according to the physical logic, such as the oilfield gate, warehouse entrance, warehouse interior, mine inspection points, and so on. These sub-areas do not overlap with each other and constitute a coverage of the effective area of the oilfield.

Then, we associate the camera with these sub-areas. The physical installation location of a fixed camera determines which sub-area it will be associated. For mobile cameras, due to its flexibility, managers can freely designate patrol routes, which means that it will be associated with multiple sub-areas, depending on the specific location corresponding to the current video frame. We make the mobile camera cover the sub-area not covered by the fixed camera as much as possible.

The single object tracking algorithm on the mobile camera reports the number of objects detected by the camera at the current position to the central camera. Although this detection task can also be performed by the object detection framework, the counting based on object tracking can reduce the influence of the outlier detection results of individual frames caused by short-term occlusion and other factors, and improve the robustness of counting. The detection result is combined with the area corresponding to the current coordinates of the mobile camera to give information about the number of objects in the area.

Although the fixed camera video is processed by the central server, similar information about the number of objects in the area can also be obtained. In addition, the cross-camera analysis of the central camera on the multi-channel fixed camera video stream can obtain the liquidity relationship of the object between the regions. Considering that there are multiple cameras associated with the same sub-area, in order to convert the liquidity information between cameras into inter-area liquidity information, it is also necessary to filter the object liquidity in the same sub-area and merge the liquidity between sub-areas with the same starting point or end point. Besides, since we have marked the “entrance” camera, we can also get information about the entry and exit of person and vehicles in the oilfield area.

In general, our description of the liquidity within the oilfield mainly includes: (a) Information about the number of objects counted within the scope of a fixed or moving camera at a certain time; (b) Information about the movement of the object across the camera; (c) People, vehicles, and so on. entering and leaving the oilfield region information; (d) The estimated total number of each object in the oilfield by summarizing above data. These information can be used by managers to monitor the status of the oilfield in real time, and can be used by other advanced analysis programs.

3.1 Configuration

We deployed the LinkStream system in the oilfield area. Approximately two hundred cameras are deployed in various areas of the oilfield, covering almost all key areas. From the perspective of deployment and application, the system adopts layered architecture, which consists of three parts: terminal device layer, edge server layer, and user layer. The terminal equipment layer includes the network camera and other terminal equipment; The edge server layer is composed of the analysis server with GPU and the NAS storage server. The user layer refers to the front-end display devices such as desktops and laptops used by users to access intelligent video analysis systems. We use Hikvision cameras as fixed cameras, and connect to the Nvidia Jetson AGX Xavier development board as the edge device side. We invited 10 patrol officers to hold mobile phones as mobile cameras. We use a linux server cluster equipped with NVIDIA Tesla T4 as the edge server for processing video stream data.

In addition, the staff drive the video vehicle to the oil tubing stack yard in the open area to shoot the object of the oil tubing for material inventory. It should be noted that the video capture vehicle needs to keep a certain distance from the tubing stack, and try not to turn around near the tubing stack, otherwise the running accuracy may be reduced. It will be discussed in detail later. Besides, the speed of the vehicle needs to be controlled within a certain range to avoid the phenomenon of video stream.

In terms of software implementation, we use the DeepStream framework as the main framework for the central server to process video streams, and make several modifications based on the official version to support frame filtering, automated deployment, message control, time stamp synchronization, and other functions. We implemented the frame filtering algorithm based on some open source code [19]. For object tracking tasks, we chose the YOLObile framework in mobile and use YOLOv5l framework optimized for small objects in central server as the object detection framework, and retrained in our real samples. The object tracking part on mobile camera uses the DeepSORT algorithm. In addition, we use lightweight ReID algorithm [9] to reduce the resource consumption of the system. Next, we will evaluate the performance of LinkStream on different performance indicators and make some comparisons, and finally show a schematic diagram of the system.

3.2 Modular Architecture

The system adopts a modular architecture and uses Docker containerization to construct, deploy and manage each module, which can isolate the environment of each module and facilitate system deployment and migration of each module. Video playback module saves RTSP stream slices into TS fragments based on GStreamer to provide video playback services. The edge video analysis module is based on the DeepStream framework and the edge video analysis method mentioned above for intelligent analysis of video streams.

The above three modules will send the generated video footage information and video analysis results to the Kafka message communication module in real time during operation. The large-scale streaming data processing module based on Flink framework will receive Kafka messages in real time and save the message data to MySQL database and Redis cache database in real time after processing. In the process of processing stream data, the second analysis algorithm of video analysis results such as perimeter protection and violation event monitoring is also run, and the final analysis results are also stored in the MySQL database and Redis cache database. The Django back-end service module provides the back-end foundation of the system website, interacts with the MySQL database, and provides RESTful APIs for other modules to call. The data communication module uses the WebSocket protocol to transmit analysis data in real time to the front-end service module through a long connection. The front-end service module is implemented by Vue.js framework, and data interaction is carried out between the front and back ends through RESTful APIs. In addition, Nginx provides reverse proxy services to manage all network ports within the system in a unified manner, and exposes only one unified port to the outside.

3.3 Bandwidth Consumption

We only measured the impact of mini-tracking algorithms and frame filtering algorithms running on the camera on network bandwidth because the traffic interaction only includes the video stream and additional data.

For a 1080 p, 30 fps video stream shot by a mobile phone, due to the movement of the camera, the video stream is not same images frame by frame, and more than 100 Mb of original video data will be generated every minute. The cellular network in the real oilfield is only 1 Mbps, so video data cannot be transmitted in time. A small tracking algorithm can save this part of the bandwidth, and only transmit object statistical data that does not exceed 1 Kbps. For fixed cameras, the frame filtering algorithm only appends an additional byte of data per frame, which can be ignored for fixed optical fibers.

After the optimization of this design, the optical fiber data received by the central server every minute is almost unchanged, still at 1 Gb (200 channels, fixed picture saves bit rate), and the cellular network data has been reduced from more than 1 G (10 patrol personnel) to less than 1 M.

3.4 System Delay

The delays of different parts of the system have different effects on the overall delay of the system. We will discuss the different components separately.

3.4.1 Mini-tracking and Crowd Counting Delay.

The retrained YOLObite + DeepSORT model can perform object tracking at an average speed of 15 FPS on the mobile phone. We noticed that the speed would change greatly with the number of objects in the image. If there is no object in the field of view, the detection speed can exceed 20 FPS; when the number of objects is large (such as a large number of oil tubing in the field of view), the detection speed will be as low as 10 FPS or even lower.

We divided the interval according to the different number of objects in the image to test the corresponding detection speed. We use each frame in the video as an image for the experiment, and give the proportion of each interval in the video. The results are shown in Table 1. Figure 7 shows the change curve of the algorithm processing speed when passing a group of object dense areas.

Table 1.

Object number	Average FPS	Proportion
0	24.62	19.0%
1–10	22.76	13.8%
11–20	20.87	16.7%
21–30	18.73	22.3%
31–40	15.32	15.2%
41–50	11.46	8.7%
>50	6.89	4.2%

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Table 1. Results of Speed

Fig. 7.

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Statistical analysis of the patrol video shows that most of the areas where the patrol personnel pass are few objects. Therefore, for the 30-frame original video stream, we adopt the strategy of sampling every other frame and only input the 15 fps video stream into the processing pipeline. Experiments show that this will not cause video congestion in a time window, and the maximum processing delay of processing results in densely-objected areas is about 53 s.

3.4.3 Overall Delay and Comparison.

In addition to the main components above, the overall delay of the system also includes the processing delay of the frame filtering algorithm, the transmission delay of the network (wired link and cellular network), video stream encoding and decoding delay, stream redirection delay (system internal resource scheduling), database cost of query. Figure 8 shows the delay of the different components of the system and the overall analysis delay from the generation of the video stream to the analysis result. In addition, Figure 8 also compares the system analysis latency without our optimization strategy.

Fig. 8.

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As can be seen, due to the high transmission delay and internal system delay, and the cluster has sufficient computing power, our design has limited optimization of the overall system delay. But it can better save system resource consumption and ensure operational robustness.

3.5 Accuracy

3.6 Resource Consumption

We compared the cost of system resources under different camera numbers, where \(x F + y M\) means to connect x fixed camera and y mobile camera. Figure 9 shows the bandwidth overhead, which has been explained in Section 3.3. Figure 10 shows the relative occupancy rate of the GPU decode unit. The comparison object is a system that has not adopted an optimization strategy. The save of decode unit is mainly because the calculation on the mobile camera reduces the number of video streams that the central server needs to process. In addition, under the condition of 200 F+10 M, the cost of GPU computing resources is reduced to 31% of the original. The save of computing unit is mainly due to the frame filtering algorithm and search space tailoring based on spatio-temporal correlation, which greatly reduces the amount of calculation performed by the central server.

Fig. 9.

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Fig. 10.

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5.1 Index Selection

Delay and precision are the most important indexes of target detection. For target detection tasks, it is hoped that the delay can be reduced as much as possible, and the precision can be improved as much as possible. In order to measure the detection effect more objectively, this study chooses precision minus delay as utility function. In this way, the accuracy is improved or the delay is reduced, the detection effect is optimized, and the utility function value is also increased. The construction of utility function value makes the overall analysis effect of the task can be quantified, so that the increase of utility function value and analysis performance optimization can be established.

In order to improve the overall analysis performance of the system, the sum of utility function values of different RTSP video streams should be maximized. Although the utility function is defined as precision minus delay, it needs to multiply a coefficient before delay due to the difference in importance degree, order of magnitude or difference and other factors. However, this coefficient is not invariable, different tasks have different tradeoffs of delay and accuracy, and the coefficient will be different to reflect the differential requirements of different tasks. In addition, although the overall analysis effect is measured by the summation of the utility function values of different RTSP video stream analysis, the importance of different tasks is also different, so the final sum should be weighted summation to reflect the priority of different tasks. The analysis effect of the more important task can better represent the overall analysis effect. Therefore, there should also be more weight in the weighted sum.

5.2 Wireless Networking

We address the network delay with wireless Mesh network (WMN), which is a network composed of a group of wireless nodes distributed in a Mesh topology in space, and the nodes are connected by wireless multi-hop mode to form a de-centralization communication architecture [2]. It is a special form of wireless Ad-Hoc network (WANET), so it inherits the characteristics of no center, no infrastructure, multi-hop, self-organization, and so on. Different from traditional wireless networks, WMN relies on a fixed access point AP, and each client communicates with AP in single-hop mode, which greatly limits the coverage range. The former uses a multi-hop communication mode, and the nodes are forwarded through multiple intermediate nodes, so as to achieve a more flexible networking structure, higher communication bandwidth and network congestion avoidance.

In response to the lack of visibility in self-organizing IoT management, this project develops an intelligent network management platform that integrates network fault diagnosis, network automatic configuration, network visualization and other network supervision requirements to support the long-term safe and stable operation of large-scale self-organizing IoT. The platform provides route security protection for Mesh networks, enhances the security of HWMP routing algorithm in Mesh networks, and realizes tamper-proof and authenticable routing information.

5.3 Resource Allocation

The resource monitoring module will monitor the GPU resource usage on the edge server, estimate the total detection frame rate that the system can carry, and pass it to the dynamic scheduling module. The effect evaluation module receives the final analysis results returned by the terminal device, and performs the following two tasks: one is to calculate the target motion speed in each time slice; the other is to evaluate the analysis effect regularly to update the relationship function between the accuracy rate and the detection frame rate of each video stream under different target motion speeds, and pass the evaluation results to the dynamic scheduling module. The dynamic scheduling module is responsible for implementing the dynamic scheduling algorithm, obtaining the optimal detection frame rate corresponding to each video stream in each time slice, and passing it to the video frame control module. The video frame control module will filter the decoded frames of each video stream according to the detection frame rate given by the dynamic scheduling module, and only retain the frames that need to perform detection tasks. Finally, these frames will be mixed according to the set batch size and then transmitted to the target detection module.

5.4 Scene Expansion

When deploying a large-scale multi-camera video analysis system to a new environment, the engineering team needs to conduct a comprehensive site survey and planning, understanding camera layout, lighting conditions, and scene characteristics. Subsequently, they will design a network architecture adapted to the large-scale transmission of video data and plan sufficient bandwidth, while adjusting the data storage system to handle the substantial data in the new scenario. Video analysis algorithms require optimization to adapt to the specific features of the new environment, and real-time processing and response systems, as well as performance testing, are deployed to ensure the system operates efficiently in the new setting. The overall goal of the process is to optimize the system configuration in the new environment, enabling it to reliably handle video data and improve algorithm accuracy and system performance.