# Analysis of People Flow Image Detection System Based on Computer Vision Sensor

Authors: Bing Ou; Jingjing Yang; Wei Wang
Journal: Journal of Sensors
 (2022)

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## Abstract

In order to meet the needs of accurately grasping the situation of people in the mall at all times, the author proposes an analysis method based on computer vision for people flow image detection system. This method combines the HOG feature with the SVM classifier, detects pedestrians through dual cameras, and builds an experimental research platform for dual-camera joint detection of pedestrians. The result shows that the error rate of human flow detected by the author’s method is the lowest of 0% and the highest of 6.25%.Conclusion. This method has a good effect on the statistics of the number of people in the shopping mall and can reduce the workload of the monitoring personnel in the shopping mall.


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## Body

## 1. Introduction

Computer vision is an emerging discipline, which is to collect video image data through cameras, apply computer vision to identify, track and measure targets in video images instead of human eyes, further do graphics processing, and make computer processing become more suitable for human eyes to observe or transmit images to instruments for detection [1]. The shape, position, speed, size, and other data of objects in video images can be extracted by computer and can be widely used in finance, justice, military, public security, border inspection, government, aerospace, electricity, factories, education, medical, and other industries. Computer vision is a comprehensive subject and a challenging and important research field, involving computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology, and cognitive science [2].People flow statistics based on machine vision is an important application field of computer vision technology, with the selection and updating of computer chips and the continuous optimization of image processing algorithms; people counting technology based on machine vision is also changing with each passing day [3]. The video image of the monitored area is collected by the camera, and the collected video image is processed by relying on the extraordinary data processing capability of the computer. First, the video image is converted into a sequence image, and the sequence image is preprocessed to ensure the accuracy of the collection, extracting the moving objects in the sequence images, identifying whether the moving objects are pedestrians, and establishing and tracking pedestrian trajectories and can not only complete the statistics of people flow but also perform statistical analysis on related information to realize the behavior analysis of pedestrian objects.With the continuous development of the economy, various large-scale transportation hubs and various public places have appeared, and the passenger flow has caused great pressure on the transportation hubs and public places; if passenger flow statistics are carried out for commercial places, operators can make scientific and effective decisions through the data of passenger flow statistics, thereby increasing the profits of operators. Counting the flow of people in cultural and entertainment places such as scenic spots can count the changes in the flow of people in real time and then get the trend of off-season and peak season, which is convenient to establish a safety warning mechanism [4]. Real-time scheduling and management can be carried out through passenger flow statistics of subway stations, airports, and other transportation hubs. The passenger flow statistics of mobile vehicles such as buses and subways can be used for early warning management of overload behavior [5].Therefore, whether it is in the fields of security or business, the technology of people flow statistics based on computer vision is of great significance, and the research on related technologies has important practical value [6].


## 2. Literature Review

In the early days, people flow statistics adopted manual counting, infrared sensing technology, and gate system; with economic development and scientific progress, the flow of people in various occasions has gradually increased, and traditional passenger flow statistics cannot meet the current needs; at this stage, most of the people flow statistics systems are developed into machine vision-based passenger flow statistics systems. In the field of human flow detection based on computer vision, many research institutions have conducted in-depth research on it in recent years and not only achieved rich results in theory but also introduced a human flow detection system suitable for different scenarios in the application field [7]. As the core technology of pedestrian flow detection, pedestrian detection has been highly concerned by scholars decades ago. The human flow detection system based on computer vision uses digital image processing technology, combined with various gradually updated and improved video image processing algorithms to analyze and process video images, and uses pattern recognition technology and trajectory analysis methods to automatically detect human flow; the detection process includes pedestrian target detection and tracking, pedestrian number statistical analysis, and timely warning of sudden changes in pedestrian flow. The demand for applications has promoted the development of technology, and people flow detection has a great demand in many public places [8].The interframe difference method proposed by Sun et al. extracts moving objects by the difference between consecutive frame images; this method is the most simple and direct and can quickly detect changes in video images [9]. The optical flow method proposed by Schmidt and Sutton forms the motion field of the whole image by obtaining the velocity vector of each pixel in the image and obtains the position area of the moving target according to the change of the optical flow vector [10]. Li et al. adopted a linear weighting method for foreground ablation and fused this ablation mechanism and background reconstruction into a Gaussian model, but the foreground ablation time of this method was too long, requiring multiple frames to completely ablate [11]. Pei et al. make full use of the neighborhood information of pixels; a foreground detection method based on dynamic background difference is proposed, which ignores the temporal continuity of pixels while considering the spatial information of pixels, resulting in low detection accuracy [12]. Mavredakis et al. proposed a new foreground extraction method, which greatly reduces the computational complexity of the original method, thereby speeding up object detection [13]. Khan et al. implemented the previously proposed HOG feature calculation using the integral graph idea, which greatly improved the detection speed [14].Based on the analysis of the computer vision human flow image detection system, the author detects and counts pedestrians under the condition of no surveillance and builds an experimental research platform for dual-camera joint detection of pedestrians, which has achieved good results.


## 3. Research Methods

### 3.1. Image Preprocessing

Due to the influence of uncertain factors such as lighting and shadows, there will always be noise in the video sequence captured by the camera. The preprocessing of the image can effectively suppress the noise of the image; the process of image preprocessing is to read each frame of image captured by the camera, then grayscale the image, and filter the final grayscale image. Image preprocessing is to filter the edge information of the image to make the image easier to detect, that is, image filtering [15–17].The main purpose of image filtering is to remove information that is not related to detection, such as illumination; this step occupies the largest proportion in image preprocessing, and the effect of filtering is also an important evaluation of the entire system. There are many filtering methods that are often used; the method used by the author is the Gaussian filtering method, which can be easily implemented in OpenCV; the GaussianBlur function used is as follows: C++: void GaussianBlur (InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY =0, int borderType).


### 3.2. Detection of Moving Objects

Optical flow method and frame difference method are effective and common methods for moving object detection; these methods are used to detect moving objects; no matter what, it is good to “see” moving objects. The core of these methods is to use the difference between video frames to determine whether there is a moving object. Among these methods, the adjacent frame difference method has strong robustness and is suitable for the system of people flow detection in shopping malls [18]. This method mainly uses the pixels corresponding to the adjacent two frames of sequence images to perform a difference operation; if the obtained difference is greater than a given threshold, it is determined that there is a moving target in the current scene; otherwise, there is no moving target; the formulas of the operation formula (1) and formula (2) are as follows.
(1)Dkx,y=Hk+1x,y−Hkx,y,(2)Tkx,y=1,Dkx,y≥T,0,Dkx,y<T,where Hkx,y is the pixel of the image sequence and T is a predetermined threshold; if Dkx,y≥T, then it is said that there is a moving target. If Dkx,y<T, then it is said that there is no moving target.


### 3.3. Pedestrian Detection

The research on pedestrian detection methods is an important and difficult point in the computer field. Computer vision is a science that studies how to make machines replace human eyes to recognize objects; in pedestrian detection, it is to study how to use a PC to see objects through sensors such as cameras, determine whether it is a pedestrian, and finally make statistics. In general, there are currently three best methods for pedestrian detection. The improved HOG+improved SVM, HOF + CSS + AdaBoost, and HOG+LBP + SVM methods have their own characteristics and are often used in combination. For the actual situation, the author uses the improved HOG + improved SVM method considering the characteristics of the shopping mall.


#### 3.3.1. HOG Features

HOG is a very effective method to mine image information. This image information is called gradient information. By calculating this gradient information, the information is represented by a histogram, which constitutes the HOG feature. The HOG feature detection method was proposed by the French researcher Dalal at the CVPR conference in 2005; after continuous improvement by experts and scholars, the current improved HOG feature was obtained. The usual case is to use the improved HOG feature with the improved SVM classifier to comprehensively detect pedestrians [19].Since the gradient usually exists at the boundary, it is necessary to use many images as samples to extract the HOG features of pedestrians, which is equivalent to the description of the local area in the image. The main idea of HOG feature is the strategy of dividing the whole into zero, dividing an image whose features are to be extracted into many small regions, counting HOG features in these small regions, and finally combining the small features to obtain the overall HOG feature picture. The extraction of HOG features is an operation in a small local connected area; changes in some areas will not have a great impact on the HOG features of the entire image, so the detection method has strong robustness and is very suitable for pedestrians. Target is detected.The flow chart of the HOG feature extraction is shown in Figure1.Figure 1
HOG feature extraction process.The specific process is as follows:(1)
Since the color information has little effect, grayscale processing is performed on the image to reduce the amount of information processed by the computer(2)
Use the correction method to correct the pixels of the image, and the method used is the gamma correction method. The formula of this method is shown in formula (3), where gamma = 1/2(3)Hx,y=Hx,ygamma(3)
Calculate the gradient of each pixel in the image, where the gradient calculation method at the pixel pointx,y is as follows:(4)Gxx,y=Hx+1,y−Hx−1,y,Gyx,y=Hx,y+1−Hx,y−1.The gradient magnitude and gradient direction are as follows:(5)Gx,y=Gxx,y2+Gyx,y2,αx,y=tan−1Gyx,yGxx,y(4)
Divide the entire image into many small squares called cells. And connect some fixed cells into a block(5)
Extract the information of the HOG feature of each block, and use 9 bins to count the HOG features of the image; that is, divide 0° ~180° into 9 parts equally; the gradient direction of each pixel isαx,y, and which bin it belongs to, the information of the pixel is placed in this bin(6)
Connect the small ones into large ones, and get all the directional gradient information of the image


#### 3.3.2. SVM Classifier

Pattern recognition technology refers to the recognition technology of various things based on computer technology to realize machine simulation of human beings; it is a branch of artificial intelligence. At present, pattern recognition technology is developing rapidly, and various theories have been proposed and continuously improved; at this stage, people’s views on pattern recognition are not unified; some people think that pattern recognition technology has inherent deficiencies in simulating people; further development is very limited. The success achieved at this stage is mainly for some simple purposes; for complex operations in complex scenarios, the workload required by pattern recognition technology is not generally large; another part of people believes that with the improvement of hardware technology and the progress of computer processing power, although the software seems to have touched the ceiling, its potential is immeasurable. Although at the theoretical level, pattern recognition technology seems to have entered a bottleneck, but in practical applications, it has achieved good results in many fields, and these results also encourage people to continue to invest in research in this field. In statistical pattern recognition, predecessors have made various attempts and proposed excellent algorithm mechanisms such as Bayesian decision-making and BP neural network, but they each have obvious shortcomings, so that they often fall into one way or another in practical applications in a vicious circle. The problem of probability density seriously restricts the promotion of the algorithm, because in terms of pattern recognition, the estimation of probability density is often more difficult to solve, and the basis of engineering practice is often less than the development of theory; as a result, some algorithms have obvious strong effects in theoretical experimental environment, but they will encounter unexpected setbacks in specific applications. However, with the breakthrough achievements of statistical theory, especially the establishment of modern statistical theory, the SVM algorithm technology of support vector machine was finally born [20].SVM, or support vector machine, is a classification method used in the field of computer vision, and it is a classification method defined by the classification hyperplane. The author uses the SVM classifier mainly to classify the HOG features of pedestrian faces, that is, after sufficient positive samples (images containing faces) and negative samples (images without faces) are provided, and the HOG features are extracted from these positive and negative samples; the SVM classifier is used for classification and identification. A diagram of the interpretation of the SVM classifier is shown in Figure2. Find the optimal separating hyperplane from the preorganized positive and negative data sets, and extract the region of interest. The mentioned support vectors are also some data closest to this separating hyperplane.Figure 2
SVM classifier.After using the improved HOG + improved SVM method, for the video frames captured by the camera, we can directly call this method to scan the image continuously, perform template matching, and then determine whether there is a pedestrian.


## 3.1. Image Preprocessing

Due to the influence of uncertain factors such as lighting and shadows, there will always be noise in the video sequence captured by the camera. The preprocessing of the image can effectively suppress the noise of the image; the process of image preprocessing is to read each frame of image captured by the camera, then grayscale the image, and filter the final grayscale image. Image preprocessing is to filter the edge information of the image to make the image easier to detect, that is, image filtering [15–17].The main purpose of image filtering is to remove information that is not related to detection, such as illumination; this step occupies the largest proportion in image preprocessing, and the effect of filtering is also an important evaluation of the entire system. There are many filtering methods that are often used; the method used by the author is the Gaussian filtering method, which can be easily implemented in OpenCV; the GaussianBlur function used is as follows: C++: void GaussianBlur (InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY =0, int borderType).


## 3.2. Detection of Moving Objects

Optical flow method and frame difference method are effective and common methods for moving object detection; these methods are used to detect moving objects; no matter what, it is good to “see” moving objects. The core of these methods is to use the difference between video frames to determine whether there is a moving object. Among these methods, the adjacent frame difference method has strong robustness and is suitable for the system of people flow detection in shopping malls [18]. This method mainly uses the pixels corresponding to the adjacent two frames of sequence images to perform a difference operation; if the obtained difference is greater than a given threshold, it is determined that there is a moving target in the current scene; otherwise, there is no moving target; the formulas of the operation formula (1) and formula (2) are as follows.
(1)Dkx,y=Hk+1x,y−Hkx,y,(2)Tkx,y=1,Dkx,y≥T,0,Dkx,y<T,where Hkx,y is the pixel of the image sequence and T is a predetermined threshold; if Dkx,y≥T, then it is said that there is a moving target. If Dkx,y<T, then it is said that there is no moving target.


## 3.3. Pedestrian Detection

The research on pedestrian detection methods is an important and difficult point in the computer field. Computer vision is a science that studies how to make machines replace human eyes to recognize objects; in pedestrian detection, it is to study how to use a PC to see objects through sensors such as cameras, determine whether it is a pedestrian, and finally make statistics. In general, there are currently three best methods for pedestrian detection. The improved HOG+improved SVM, HOF + CSS + AdaBoost, and HOG+LBP + SVM methods have their own characteristics and are often used in combination. For the actual situation, the author uses the improved HOG + improved SVM method considering the characteristics of the shopping mall.


### 3.3.1. HOG Features

HOG is a very effective method to mine image information. This image information is called gradient information. By calculating this gradient information, the information is represented by a histogram, which constitutes the HOG feature. The HOG feature detection method was proposed by the French researcher Dalal at the CVPR conference in 2005; after continuous improvement by experts and scholars, the current improved HOG feature was obtained. The usual case is to use the improved HOG feature with the improved SVM classifier to comprehensively detect pedestrians [19].Since the gradient usually exists at the boundary, it is necessary to use many images as samples to extract the HOG features of pedestrians, which is equivalent to the description of the local area in the image. The main idea of HOG feature is the strategy of dividing the whole into zero, dividing an image whose features are to be extracted into many small regions, counting HOG features in these small regions, and finally combining the small features to obtain the overall HOG feature picture. The extraction of HOG features is an operation in a small local connected area; changes in some areas will not have a great impact on the HOG features of the entire image, so the detection method has strong robustness and is very suitable for pedestrians. Target is detected.The flow chart of the HOG feature extraction is shown in Figure1.Figure 1
HOG feature extraction process.The specific process is as follows:(1)
Since the color information has little effect, grayscale processing is performed on the image to reduce the amount of information processed by the computer(2)
Use the correction method to correct the pixels of the image, and the method used is the gamma correction method. The formula of this method is shown in formula (3), where gamma = 1/2(3)Hx,y=Hx,ygamma(3)
Calculate the gradient of each pixel in the image, where the gradient calculation method at the pixel pointx,y is as follows:(4)Gxx,y=Hx+1,y−Hx−1,y,Gyx,y=Hx,y+1−Hx,y−1.The gradient magnitude and gradient direction are as follows:(5)Gx,y=Gxx,y2+Gyx,y2,αx,y=tan−1Gyx,yGxx,y(4)
Divide the entire image into many small squares called cells. And connect some fixed cells into a block(5)
Extract the information of the HOG feature of each block, and use 9 bins to count the HOG features of the image; that is, divide 0° ~180° into 9 parts equally; the gradient direction of each pixel isαx,y, and which bin it belongs to, the information of the pixel is placed in this bin(6)
Connect the small ones into large ones, and get all the directional gradient information of the image


### 3.3.2. SVM Classifier

Pattern recognition technology refers to the recognition technology of various things based on computer technology to realize machine simulation of human beings; it is a branch of artificial intelligence. At present, pattern recognition technology is developing rapidly, and various theories have been proposed and continuously improved; at this stage, people’s views on pattern recognition are not unified; some people think that pattern recognition technology has inherent deficiencies in simulating people; further development is very limited. The success achieved at this stage is mainly for some simple purposes; for complex operations in complex scenarios, the workload required by pattern recognition technology is not generally large; another part of people believes that with the improvement of hardware technology and the progress of computer processing power, although the software seems to have touched the ceiling, its potential is immeasurable. Although at the theoretical level, pattern recognition technology seems to have entered a bottleneck, but in practical applications, it has achieved good results in many fields, and these results also encourage people to continue to invest in research in this field. In statistical pattern recognition, predecessors have made various attempts and proposed excellent algorithm mechanisms such as Bayesian decision-making and BP neural network, but they each have obvious shortcomings, so that they often fall into one way or another in practical applications in a vicious circle. The problem of probability density seriously restricts the promotion of the algorithm, because in terms of pattern recognition, the estimation of probability density is often more difficult to solve, and the basis of engineering practice is often less than the development of theory; as a result, some algorithms have obvious strong effects in theoretical experimental environment, but they will encounter unexpected setbacks in specific applications. However, with the breakthrough achievements of statistical theory, especially the establishment of modern statistical theory, the SVM algorithm technology of support vector machine was finally born [20].SVM, or support vector machine, is a classification method used in the field of computer vision, and it is a classification method defined by the classification hyperplane. The author uses the SVM classifier mainly to classify the HOG features of pedestrian faces, that is, after sufficient positive samples (images containing faces) and negative samples (images without faces) are provided, and the HOG features are extracted from these positive and negative samples; the SVM classifier is used for classification and identification. A diagram of the interpretation of the SVM classifier is shown in Figure2. Find the optimal separating hyperplane from the preorganized positive and negative data sets, and extract the region of interest. The mentioned support vectors are also some data closest to this separating hyperplane.Figure 2
SVM classifier.After using the improved HOG + improved SVM method, for the video frames captured by the camera, we can directly call this method to scan the image continuously, perform template matching, and then determine whether there is a pedestrian.


## 3.3.1. HOG Features

HOG is a very effective method to mine image information. This image information is called gradient information. By calculating this gradient information, the information is represented by a histogram, which constitutes the HOG feature. The HOG feature detection method was proposed by the French researcher Dalal at the CVPR conference in 2005; after continuous improvement by experts and scholars, the current improved HOG feature was obtained. The usual case is to use the improved HOG feature with the improved SVM classifier to comprehensively detect pedestrians [19].Since the gradient usually exists at the boundary, it is necessary to use many images as samples to extract the HOG features of pedestrians, which is equivalent to the description of the local area in the image. The main idea of HOG feature is the strategy of dividing the whole into zero, dividing an image whose features are to be extracted into many small regions, counting HOG features in these small regions, and finally combining the small features to obtain the overall HOG feature picture. The extraction of HOG features is an operation in a small local connected area; changes in some areas will not have a great impact on the HOG features of the entire image, so the detection method has strong robustness and is very suitable for pedestrians. Target is detected.The flow chart of the HOG feature extraction is shown in Figure1.Figure 1
HOG feature extraction process.The specific process is as follows:(1)
Since the color information has little effect, grayscale processing is performed on the image to reduce the amount of information processed by the computer(2)
Use the correction method to correct the pixels of the image, and the method used is the gamma correction method. The formula of this method is shown in formula (3), where gamma = 1/2(3)Hx,y=Hx,ygamma(3)
Calculate the gradient of each pixel in the image, where the gradient calculation method at the pixel pointx,y is as follows:(4)Gxx,y=Hx+1,y−Hx−1,y,Gyx,y=Hx,y+1−Hx,y−1.The gradient magnitude and gradient direction are as follows:(5)Gx,y=Gxx,y2+Gyx,y2,αx,y=tan−1Gyx,yGxx,y(4)
Divide the entire image into many small squares called cells. And connect some fixed cells into a block(5)
Extract the information of the HOG feature of each block, and use 9 bins to count the HOG features of the image; that is, divide 0° ~180° into 9 parts equally; the gradient direction of each pixel isαx,y, and which bin it belongs to, the information of the pixel is placed in this bin(6)
Connect the small ones into large ones, and get all the directional gradient information of the image


## 3.3.2. SVM Classifier

Pattern recognition technology refers to the recognition technology of various things based on computer technology to realize machine simulation of human beings; it is a branch of artificial intelligence. At present, pattern recognition technology is developing rapidly, and various theories have been proposed and continuously improved; at this stage, people’s views on pattern recognition are not unified; some people think that pattern recognition technology has inherent deficiencies in simulating people; further development is very limited. The success achieved at this stage is mainly for some simple purposes; for complex operations in complex scenarios, the workload required by pattern recognition technology is not generally large; another part of people believes that with the improvement of hardware technology and the progress of computer processing power, although the software seems to have touched the ceiling, its potential is immeasurable. Although at the theoretical level, pattern recognition technology seems to have entered a bottleneck, but in practical applications, it has achieved good results in many fields, and these results also encourage people to continue to invest in research in this field. In statistical pattern recognition, predecessors have made various attempts and proposed excellent algorithm mechanisms such as Bayesian decision-making and BP neural network, but they each have obvious shortcomings, so that they often fall into one way or another in practical applications in a vicious circle. The problem of probability density seriously restricts the promotion of the algorithm, because in terms of pattern recognition, the estimation of probability density is often more difficult to solve, and the basis of engineering practice is often less than the development of theory; as a result, some algorithms have obvious strong effects in theoretical experimental environment, but they will encounter unexpected setbacks in specific applications. However, with the breakthrough achievements of statistical theory, especially the establishment of modern statistical theory, the SVM algorithm technology of support vector machine was finally born [20].SVM, or support vector machine, is a classification method used in the field of computer vision, and it is a classification method defined by the classification hyperplane. The author uses the SVM classifier mainly to classify the HOG features of pedestrian faces, that is, after sufficient positive samples (images containing faces) and negative samples (images without faces) are provided, and the HOG features are extracted from these positive and negative samples; the SVM classifier is used for classification and identification. A diagram of the interpretation of the SVM classifier is shown in Figure2. Find the optimal separating hyperplane from the preorganized positive and negative data sets, and extract the region of interest. The mentioned support vectors are also some data closest to this separating hyperplane.Figure 2
SVM classifier.After using the improved HOG + improved SVM method, for the video frames captured by the camera, we can directly call this method to scan the image continuously, perform template matching, and then determine whether there is a pedestrian.


## 4. Analysis of Results

The experimental system uses two cameras to capture the faces of pedestrians entering and exiting the mall and transmits the obtained data to the PC, where it is processed and the flow of people in the mall is obtained. Figure3 is a physical simulation diagram. The processing flow of the PC includes several parts such as calling the camera to take images, filtering processing, moving object detection, face detection, and people counting. The author’s overall block diagram is shown in Figure 4.Figure 3
Physical simulation diagram.Figure 4
Overall block diagram of the system.The idea designed by the author is shown in Figure4; the pedestrians in the images captured by the two cameras are counted separately, the number of pedestrians collected and counted by camera 1 is the number of people entering the mall, the number of pedestrians collected and counted by the camera 2 is the number of people leaving the shopping mall, and the difference between these two data is the pedestrian flow in the shopping mall. Through experimental simulation, a certain classroom is used as a place to simulate a shopping mall, and the number of people in the classroom at each time of the day is obtained, as shown in Table 1. The error ratios for each time period are shown in Figure 5. By analyzing and comparing the obtained data, the data obtained by the author’s method is not much different from the actual data, which can well replace the responsibilities of the staff.Table 1
Statistics of people.
Time (t)8 : 0010 : 0012 : 0014 : 0016 : 00Number of people counted by author’s method1028152025The actual number1027161924Figure 5
Error ratio of each time period.


## 5. Conclusion

In the response measures of emergency incidents in shopping malls, to minimize casualties, it is necessary to accurately grasp the situation of personnel in shopping malls at all times. In order to reduce casualties, by combining the HOG feature with the SVM classifier, the author uses dual cameras to detect pedestrians, builds an experimental research platform for dual-camera joint detection of pedestrians, and designs a system for detecting people in shopping malls. It has played a very good role in the statistics of the number of people in the shopping mall and can reduce the workload of the personnel responsible for monitoring the shopping mall. There are still some defects in this method, such as the detection effect of occluded pedestrians is not very ideal; there is still a lot of work in this system and the field of human detection and tracking to be further explored. The system is not universal and needs different training samples for different scenarios, especially when the camera angle and height change greatly. Whether it is the human body detection, identification and tracking technology used in video surveillance, or the people flow statistics system, there is a lot of room for development. With the gradual increase in the application of video surveillance and people flow statistics in people’s production and life, more and better optimization algorithms will surely be brought, which will promote greater development in this field.



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*Source: 8099876-2022-07-04.xml*