Pedestrian intention recognition based on graph convolution

A recognition method and pedestrian technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem of inability to recognize fine-grained intent, and achieve the effect of improving the active safety performance of automobiles, improving understanding ability, and protecting pedestrian safety.

Active Publication Date: 2019-01-01
SOUTHEAST UNIV
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Problems solved by technology

[0005] Purpose of the invention: The purpose of the present invention is to solve the problem that the existing image-based pedestrian recognition device and its method model are relatively simple, can only judge the position of the pedestrian and compare the time before and after passing, and judge whether the pedestrian is moving, and cannot perform fine-grained pedestrian intention recognition. question

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  • Pedestrian intention recognition based on graph convolution
  • Pedestrian intention recognition based on graph convolution
  • Pedestrian intention recognition based on graph convolution

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Embodiment

[0035] Such as Figure 1 to Figure 3 As shown, the video data parameter collected by the forward-looking camera used in the present invention is 1280×720@60FPS, and the video frame is a color image, including RGB three-channel color information, represented by a tensor of (1280,720,3) dimensions, in the tensor Each element is an integer, and the value range is [0,255];

[0036] Apply the Mask RCNN method in the paper to extract the key point information of the human body from the video frame image. The identified key points of the human body are as follows: figure 1 As shown, there are 18 key points in total, namely nose, neck, left eye, right eye, left ear, right ear, left shoulder, right button, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, Right knee, left ankle and right ankle; key point information includes two-dimensional coordinate information and recognition accuracy of each key point, each key point is represented by (x, y, c), whe...

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Abstract

The invention relates to a pedestrian intention recognition method based on graph convolution. The video image of road environment is captured by a forward-looking camera system mounted on a vehicle.Pedestrian detection and pedestrian human key points extraction are carried out on the image, and the adjacency matrix is constructed to represent the connection information of pedestrian human key points based on graph theory. The bottom feature is extracted from coordinate information and adjacency matrix representation of key points by graph convolution algorithm, and the bottom feature is extracted and time series analyzed by depth convolution neural network and depth loop neural network. Based on the data set of pedestrian intention constructed by manual labeling method, the parameters ofthe model are optimized to realize the classification and recognition of pedestrian intention. The invention effectively utilizes the high-level semantic feature of the key point information of the pedestrian human body, so that the automobile advanced driving assistance system has the ability to understand the pedestrian behavior intention.

Description

technical field [0001] The invention relates to a pedestrian intention recognition technology based on graph convolution, and belongs to the field of advanced automobile driver assistance technology. Background technique [0002] Pedestrian detection is an important function of ADAS (Advanced Driver Assistance System). Existing pedestrian detection systems use radar or cameras to detect the position of pedestrians. When pedestrians are detected on the driving route, they will decelerate and brake in time to reduce accident injuries and avoid accidents. [0003] The images and videos of the vehicle's driving environment captured by the on-board camera system contain relevant information about pedestrians and the environment. However, due to the limitations of algorithms, the current pedestrian detection system cannot understand the environment and pedestrian behavior from a high abstract level. [0004] Chinese patent application publication number CN107406071A discloses an ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/23G06V40/103G06F18/2413
Inventor 秦文虎张哲孙立博张仕超王昭东尚昊
Owner SOUTHEAST UNIV
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