CN112347938B - People stream detection method based on improved YOLOv3 - Google Patents

People stream detection method based on improved YOLOv3 Download PDF

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CN112347938B
CN112347938B CN202011236196.6A CN202011236196A CN112347938B CN 112347938 B CN112347938 B CN 112347938B CN 202011236196 A CN202011236196 A CN 202011236196A CN 112347938 B CN112347938 B CN 112347938B
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王议
袁佳
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Nanjing Institute of Mechatronic Technology
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Abstract

A people stream detection method based on improved YOLOv 3. The method comprises the following steps: step 1, acquiring experimental data of people flow monitoring; step 2, modeling the people stream monitoring problem; step 3, distinguishing the types of the traffic video monitoring images by utilizing a kNN algorithm; step 4, improving anchor frame and receptive field mechanisms of the YOLOv3 model to monitor targets; and 5, solving a people stream monitoring model. According to the invention, the traffic flow and the vehicle flow are monitored on the basis of the traffic monitoring video image, the anchor frame of the YOLOv3 model is improved by combining the regression difficulty of the model detection frame with the K-means algorithm, and meanwhile, a receptive field mechanism is added into a backbone module for extracting image features capable of reflecting deeper layers, so that the feature extraction capability of the model is improved.

Description

People stream detection method based on improved YOLOv3
Technical Field
The invention relates to the field of people flow monitoring, in particular to a people flow detection method based on improved YOLOv 3.
Background
With the rapid development of cities, people stream management and mobile mode mining in cities are becoming increasingly important. Meanwhile, with the development of various perception technologies represented by crowd sensing, concepts of smart cities are proposed, and a large amount of perception data in the smart cities provides possibilities for analyzing people streams. The regional crowd flow prediction has important strategic significance for traffic management and public safety, if the flow condition of the crowd can be predicted in advance, related departments can early warn in advance, an emergency mechanism is started, and accidents are avoided. The smart city field encompasses many leading-edge directions of research . Such as speed detection at traffic intersections, flow estimation, etc. Meanwhile, researchers predict future tracks of individuals according to the historical tracks of the individuals. However, these individual studies do not meet the existing needs if a prediction of the flow across the area is required. Urban road networks are complex, individuals move numerous and cannot be fully monitored, but related departments such as government and the like are concerned about the personnel flow condition of the whole area. Therefore, the change of the regional flow is detected in advance, guidance can be provided for related departments, intervention measures can be adopted earlier, and dangers are avoided.
Disclosure of Invention
In order to solve the problems, the invention provides a people stream detection method based on improved YOLOv3 on the basis of a YOLOv3 algorithm. In order to reduce the regression difficulty of the detection frame, the prior knowledge of the detection target is obtained by adjusting the anchor frame of the network. In addition, to enhance the importance of the features presented, a multi-receptive field mechanism is added to the backbone block to extract advanced features of the monitored image. To achieve the purpose, the invention provides a people stream detection method based on improved YOLOv3, which comprises the following specific steps:
step 1, obtaining experimental data: dividing different areas, and monitoring photos of pedestrians and vehicles in the different areas through traffic videos;
step 2, modeling of people stream monitoring problems: the people flow in a certain area is obtained by subtracting the people flow flowing out of the area from the people flow entering the area, and the people flow is divided into two types of people flow and vehicle flow;
step 3, distinguishing the types of traffic video monitoring images: dividing the traffic video monitoring images into three categories: vehicles, pedestrians, and both pedestrians and vehicles, if a photo contains both vehicles and pedestrians, the photo can be categorized into a vehicle photo and a pedestrian photo;
step 4, improving the YOLOv3 network monitoring target: the anchor frame and receptive field mechanism of the YOLOv3 network are improved to detect pedestrians and vehicles respectively;
step 5, solving a people stream monitoring model: and (3) carrying the result of the YOLOv3 detection back to the people stream monitoring problem, and solving the people stream monitoring result.
Further, the division of different regions in step 1 can be expressed as:
different areas of a city are divided, and the different areas are expressed as R= { R 1 ,r 2 ,r 3 ,...,r n Respectively collecting each region into an image to establish a training sample set D Ri And test sample set T Ri 。
Further, the modeling of the people stream monitoring problem in step 2 can be expressed as:
dividing the people stream monitoring problem into: people flow monitoring and vehicle flow monitoring are carried out on a certain area r i The people flow rate of (1) can be representedIs thatWhen a pedestrian enters the area, the flow rate superimposed on the area for the period of time is +.>When the pedestrian leaves the area, the flow rate superimposed to the area for the period of time is +.>For a certain region r i Can be expressed as the vehicle flow rate of (2)When the vehicle is driving into the area, the flow rate superimposed on the area for the period of time is +.>When the vehicle is driven out of the area, the flow rate superimposed on the area for the period of time is +.>
Further, in the step 3, the distinguishing traffic video monitoring image types is specifically described as follows:
test sample set T by kNN algorithm Ri Classification is carried out, and the classification is as follows: pedestrians, vehicles, and both pedestrians and vehicles. Searching for test sample T Ri From training sample set T Ri The nearest k samples, if most of the k training samples belong to a certain class, the test sample also belongs to this class, and the distance formula is measured using euclidean distance:
L(x i ,x j ) Is the image sample x i And image sample x j Is used for the distance of euclidean distance,is x i Characteristic value of the first dimension->Is x j The eigenvalue of the first dimension.
Further, the improved YOLOv3 network monitoring target in step 4 is specifically described as:
two YOLOv3 models are built to detect pedestrians and vehicles respectively, each model comprising three parts: the system comprises a trunk module, a feature fusion module and a prediction module, wherein the trunk module extracts rich information of an input image, the feature fusion module uses short links to splice feature graphs with different abstract scales, and finally the prediction module predicts a detection result through local and global information.
In order to reduce the regression difficulty of the detection frame, a clustering center on a training set is obtained through K-means, the anchor frame of the original Yolov3 network is reset, and the distance measurement of the following formula is used:
d＝1-IOU(b,a) (2)
b and a respectively represent a label, a clustering center frame and a clustering center frame, d represents the overlapping degree of the label frame and the clustering center frame, d is smaller, the higher the overlapping degree of the label frame and the clustering center frame is, 80 times of clustering is carried out on training data to obtain 5 clustering centers, all the YOLO anchor frames are arranged by using the clustering centers, 2 smaller anchor frames are used for a YOLO layer corresponding to a large-resolution feature map, and 3 larger anchor frames are used for a YOLO layer corresponding to a small-resolution feature map.
The backbone network has the effects that effective features of images are extracted for prediction, the quality of the features affects the final output result, a multi-receptive field mechanism is added in a backbone module to learn the rich features for learning the richer features, the learning capacity of the backbone module is improved, and a CBLP module with convolution kernel sizes of 1 multiplied by 1 and 5 multiplied by 5 is respectively connected with an original CBLP module in parallel for obtaining different receptive field features, wherein the mapping relation between the CBLP module and the multi-receptive field module is expressed as the following formula:
x CBLP ＝H(x i ) (3)
x multi ＝H 1 (x i )+H 3 (x i )+H 5 (x i ) (4)
wherein x is CBLP And x multi The outputs of the CBLP module and the multi-receptive field module are respectively represented; h 1 (·)、H 3 (. Cndot.) and H 5 (. Cndot.) the convolution kernel sizes are respectively: 1×1, 3×3, and 5×5 mappings; x is x i And representing the input characteristic diagram, and training the YOLOv3 network by using a training sample set to obtain two improved YOLOv3 networks capable of detecting pedestrians and vehicles.
Further, the solving of the people stream monitoring result in the step 5 is specifically described as follows:
the test sample images classified in the step 3 are respectively detected through the improved YOLOv3 network for detecting pedestrians and driving by training in the step 4, and when r i When a pedestrian enters the area, the area isSuperimposed on the number of people entering, when r i When pedestrians leave the area, at +.>Superimposed on the number of people leaving, when r i When a vehicle enters the area, the area is at +.>Superimposed on the number of vehicles entering, when r i When the vehicle is driving away in the area, the area is at +.>The number of vehicles leaving is superimposed, and r is finally obtained i Regional people flow->And traffic flow->
The people flow detection method based on the improved YOLOv3 has the beneficial effects that: the invention has the technical effects that:
1. the method and the device utilize the kNN algorithm to pre-classify the test set, reduce the detection time of the detection model, and improve the accuracy of the model;
2. according to the invention, the anchor frame of the YOLOv3 model is adjusted by combining the clustering center obtained by K-means training in order to reduce the regression difficulty of the detection frame;
3. according to the invention, a multi-receptive field mechanism is added into a backbone module of the YOLOv3 model to learn rich features, so that the learning capacity of the backbone module is improved;
4. the invention can accurately detect the information of the traffic flow and the traffic flow, and provides an important technical means for detecting the traffic flow.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
the invention provides a people flow detection method based on improved YOLOv3, which aims to improve the accuracy of people flow monitoring, reduce the regression difficulty of a YOLOv3 model detection frame and improve the learning capacity of a backbone module. FIG. 1 is a flow chart of the present invention. The steps of the present invention will be described in detail with reference to the flow charts.
Step 1, obtaining experimental data: dividing different areas, and monitoring photos of pedestrians and vehicles in the different areas through traffic videos;
the division of the different regions in step 1 can be expressed as:
different areas of a city are divided, and the different areas are expressed as R= { R 1 ,r 2 ,r 3 ,...,r n Respectively collecting each region into an image to establish a training sample set D Ri And test sample set T Ri 。
Step 2, modeling of people stream monitoring problems: the people flow in a certain area is obtained by subtracting the people flow flowing out of the area from the people flow entering the area, and the people flow is divided into two types of people flow and vehicle flow;
the modeling of the people stream monitoring problem in step 2 can be expressed as:
dividing the people stream monitoring problem into: people flow monitoring and vehicle flow monitoring are carried out on a certain area r i The people flow rate of (1) can be expressed asWhen a pedestrian enters the area, the flow rate superimposed on the area for the period of time is +.>When the pedestrian leaves the area, the flow rate superimposed to the area for the period of time is +.>For a certain region r i Can be expressed as the vehicle flow rate of (2)When the vehicle is driving into the area, the flow rate superimposed on the area for the period of time is +.>When the vehicle is driven out of the area, the flow rate superimposed on the area for the period of time is +.>
Step 3, distinguishing the types of traffic video monitoring images: dividing the traffic video monitoring images into three categories: vehicle pictures, pedestrian pictures and pictures containing both pedestrians and vehicles, if a photo contains both vehicles and pedestrians, the picture can be categorized into both vehicle pictures and pedestrian pictures;
and 3, distinguishing the types of traffic video monitoring images is specifically described as follows:
test sample set T by kNN algorithm Ri Classification is carried out, and the classification is as follows: pedestrians, vehicles, and both pedestrians and vehicles. Searching for test sample T Ri From training sample set T Ri The nearest k samples, if thisMost of the k training samples belong to a certain class, and the test sample also belongs to this class, the distance formula is measured using the euclidean distance:
L(x i ,x j ) Is the image sample x i And image sample x j Is used for the distance of euclidean distance,is x i Characteristic value of the first dimension->Is x j The eigenvalue of the first dimension.
Step 4, improving the YOLOv3 network monitoring target: the anchor frame and receptive field mechanism of the YOLOv3 network are improved to detect pedestrians and vehicles respectively;
the improved YOLOv3 network monitoring target in step 4 is specifically described as follows:
two YOLOv3 models are built to detect pedestrians and vehicles respectively, each model comprising three parts: the system comprises a trunk module, a feature fusion module and a prediction module, wherein the trunk module extracts rich information of an input image, the feature fusion module uses short links to splice feature graphs with different abstract scales, and finally the prediction module predicts a detection result through local and global information.
In order to reduce the regression difficulty of the detection frame, a clustering center on a training set is obtained through K-means, the anchor frame of the original Yolov3 network is reset, and the distance measurement of the following formula is used:
d＝1-IOU(b,a) (2)
b and a respectively represent a label, a clustering center frame and a clustering center frame, d represents the overlapping degree of the label frame and the clustering center frame, d is smaller, the higher the overlapping degree of the label frame and the clustering center frame is, 80 times of clustering is carried out on training data to obtain 5 clustering centers, all the YOLO anchor frames are arranged by using the clustering centers, 2 smaller anchor frames are used for a YOLO layer corresponding to a large-resolution feature map, and 3 larger anchor frames are used for a YOLO layer corresponding to a small-resolution feature map.
The backbone network has the effects that effective features of images are extracted for prediction, the quality of the features affects the final output result, a multi-receptive field mechanism is added in a backbone module to learn the rich features for learning the richer features, the learning capacity of the backbone module is improved, and a CBLP module with convolution kernel sizes of 1 multiplied by 1 and 5 multiplied by 5 is respectively connected with an original CBLP module in parallel for obtaining different receptive field features, wherein the mapping relation between the CBLP module and the multi-receptive field module is expressed as the following formula:
x CBLP ＝H(x i ) (3)
x multi ＝H 1 (x i )+H 3 (x i )+H 5 (x i ) (4)
wherein x is CBLP And x multi The outputs of the CBLP module and the multi-receptive field module are respectively represented; h 1 (·)、H 3 (. Cndot.) and H 5 (. Cndot.) the convolution kernel sizes are respectively: 1×1, 3×3, and 5×5 mappings; x is x i And representing the input characteristic diagram, and training the YOLOv3 network by using a training sample set to obtain two improved YOLOv3 networks capable of detecting pedestrians and vehicles.
Step 5, solving a people stream monitoring model: carrying back the result of the YOLOv3 detection to the people stream monitoring problem, and solving the people stream monitoring result;
the solving of the people stream monitoring result in the step 5 is specifically described as follows:
the test sample images classified in the step 3 are respectively detected through the improved YOLOv3 network for detecting pedestrians and driving by training in the step 4, and when r i When a pedestrian enters the area, the area isSuperimposed on the number of people entering, when r i When pedestrians leave the area, at +.>Superimposed and separatedThe number of people who open, when r i When a vehicle enters the area, the area is at +.>Superimposed on the number of vehicles entering, when r i When the vehicle is driving away in the area, the area is at +.>The number of vehicles leaving is superimposed, and r is finally obtained i Regional people flow->And traffic flow->
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.

Claims (4)

1. The people stream detection method based on the improved YOLOv3 comprises the following specific steps of:
step 1, obtaining experimental data: dividing different areas, and monitoring photos of pedestrians and vehicles in the different areas through traffic videos;
step 2, modeling of people stream monitoring problems: the people flow in a certain area is obtained by subtracting the people flow flowing out of the area from the people flow entering the area, and the people flow is divided into two types of people flow and vehicle flow;
step 3, distinguishing the types of traffic video monitoring images: dividing the traffic video monitoring images into three categories: vehicles, pedestrians, and both pedestrians and vehicles, if a photo contains both vehicles and pedestrians, the photo can be categorized into a vehicle picture and a pedestrian picture;
and 3, distinguishing the types of traffic video monitoring images is specifically described as follows:
test sample set T by kNN algorithm Ri Classification is carried out, and the classification is as follows: pedestrians, vehicles, and both pedestrians and vehicles; searching for test sample T Ri From training sample set T Ri The most recent k samples, if most of the k training samples belong to a certain class, the test sample also belongs to this class, measured using Euclidean distance:
L(x i ,x j ) Is the image sample x i And image sample x j Is used for the distance of euclidean distance,is x i Characteristic value of the first dimension->Is x j A feature value of the first dimension;
step 4, improving the YOLOv3 network monitoring target: the anchor frame and receptive field mechanism of the YOLOv3 network are improved to detect pedestrians and vehicles respectively;
the improved YOLOv3 network monitoring target in step 4 is specifically described as follows:
two YOLOv3 models are built to detect pedestrians and vehicles respectively, each model comprising three parts: the system comprises a trunk module, a characteristic fusion module and a prediction module; in order to reduce the regression difficulty of the detection frame, obtaining a clustering center on a training set through K-means, resetting an anchor frame of an original YOLOv3 network, and using the distance measurement of the following formula:
d＝1-IOU(b,a) (2)
b and a respectively represent a label and a clustering center frame, d represents the overlapping degree of the label frame and the clustering center frame, d is smaller, the overlapping degree of the label frame and the clustering center frame is higher, all the YOLO anchor frames are arranged by using the clustering center, 2 smaller anchor frames are used for a YOLO layer corresponding to a large-resolution feature map, and 3 larger anchor frames are used for a YOLO layer corresponding to a small-resolution feature map;
the multi-receptive field mechanism is added into the backbone module to learn rich features, the learning capacity of the backbone module is improved, and in order to obtain different receptive field features, the CBLP modules with convolution kernel sizes of 1 multiplied by 1 and 5 multiplied by 5 are respectively connected with the original CBLP modules in parallel, and the mapping relation between the CBLP modules and the multi-receptive field module is expressed as the following formula:
x CBLP ＝H(x i ) (3)
x multi ＝H 1 (x i )+H 3 (x i )+H 5 (x i ) (4)
wherein x is CBLP And x multi The outputs of the CBLP module and the multi-receptive field module are respectively represented; h 1 (·)、H 3 (. Cndot.) and H 5 (. Cndot.) the convolution kernel sizes are respectively: 1×1, 3×3, and 5×5 mappings; x is x i Representing an input feature map, training the YOLOv3 network by using a training sample set to obtain two improved YOLOv3 networks for detecting pedestrians and vehicles;
step 5, solving a people stream monitoring model: and (3) carrying the result of the YOLOv3 detection back to the people stream monitoring problem, and solving the people stream monitoring result.
2. The improved YOLOv 3-based people stream detection method of claim 1, wherein: the division of different areas in step 1 is expressed as:
different areas of a city are divided, and the different areas are expressed as R= { R 1 ,r 2 ,r 3 ,...,r n Respectively collecting each region into an image to establish a training sample set D Ri And test sample set T Ri 。
3. The improved YOLOv 3-based people stream detection method of claim 1, wherein: in the step 2, the modeling of the people flow monitoring problem is expressed as follows:
dividing the people stream monitoring problem into: people flow monitoring and vehicle flow monitoring are carried out on a certain area r i The people flow rate of (1) can be expressed asWhen a pedestrian enters the area, the flow superimposed to the area at time t is +.>When the pedestrian leaves the area, the flow rate superimposed to the area for the period of time is +.>For a certain region r i Can be expressed as +.>When the vehicle is driving into the area, the flow rate superimposed on the area for the period of time is +.>When the vehicle is driven out of the area, the flow rate superimposed on the area for the period of time is +.>
4. The improved YOLOv 3-based people stream detection method of claim 1, wherein: the solving of the people stream monitoring result in the step 5 is specifically described as follows:
the test sample images classified in the step 3 are respectively detected through the improved YOLOv3 network for detecting pedestrians and driving by training in the step 4, and when r i When a pedestrian enters the area, the area isSuperimposed on the number of people entering, when r i When pedestrians leave the area, at +.>Stacking the number of people leaving; when r is i When a vehicle enters the area, the area is at +.>Upper stackAdding the number of vehicles entering; when r is i When the vehicle is driving away in the area, the area is at +.>Overlaying the number of vehicles leaving; finally obtain r i Flow of people in an areaAnd traffic flow->
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CN111046787A (en) * 2019-12-10 2020-04-21 华侨大学 Pedestrian detection method based on improved YOLO v3 model
CN111626128A (en) * 2020-04-27 2020-09-04 江苏大学 Improved YOLOv 3-based pedestrian detection method in orchard environment

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CN110796168A (en) * 2019-09-26 2020-02-14 江苏大学 Improved YOLOv 3-based vehicle detection method
CN111046787A (en) * 2019-12-10 2020-04-21 华侨大学 Pedestrian detection method based on improved YOLO v3 model
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