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Pedestrian detection method and device, equipment and medium

A pedestrian detection and pedestrian technology, which is applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve problems such as unsatisfactory results, and achieve the effect of improving pedestrian detection speed.

Pending Publication Date: 2020-10-09
BEIJING WODONG TIANJUN INFORMATION TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the process of realizing the present invention, the inventor found that there are at least the following technical problems in the prior art: the above method has achieved good results on standard pedestrian detection data, but in occluded scenes (including intra-class occlusion, pedestrian and human occlusion, as well as inter-class occlusion, occlusion between people and objects, etc.), have not yet achieved satisfactory results

Method used

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  • Pedestrian detection method and device, equipment and medium
  • Pedestrian detection method and device, equipment and medium
  • Pedestrian detection method and device, equipment and medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0028] figure 1 It is a flowchart of a pedestrian detection method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of pedestrian detection. The method can be executed by a pedestrian detection device, and the pedestrian detection device can be implemented in software and / or hardware, for example, the pedestrian detection device can be configured in a computer device. Such as figure 1 As shown, the method includes:

[0029] S110. Acquire an image to be detected.

[0030] In this embodiment, the image to be detected may be an image requiring pedestrian detection. Wherein, the acquisition method of the image to be detected is not limited here. Optionally, the video frame captured by the camera can be directly obtained as the image to be detected, or the existing video can be processed to obtain the image to be detected for pedestrian detection.

[0031] S120. Input the image to be detected into the trained single-shot target...

Embodiment 2

[0038] figure 2 It is a flowchart of a pedestrian detection method provided by Embodiment 2 of the present invention. In this embodiment, on the basis of the foregoing embodiments, the training of a single target detector is embodied. Such as figure 2 As shown, the method includes:

[0039] S210. Acquire a sample image, a pedestrian frame labeling result corresponding to the sample image, and a pedestrian head labeling result corresponding to the sample image.

[0040] In this embodiment, the sample image may be an image containing pedestrians, preferably, may be an image containing blocked pedestrians. Manually annotate the sample image, mark the pedestrian frame and pedestrian head in the sample image, and obtain the sample image, the pedestrian frame annotation result corresponding to the sample image, and the pedestrian head annotation result corresponding to the sample image.

[0041] S220. Generate a training sample pair based on the sample image, the tagging resul...

Embodiment 3

[0059] Figure 3a It is a flowchart of a pedestrian detection method provided by Embodiment 3 of the present invention. This embodiment provides a preferred embodiment on the basis of the foregoing embodiments. Such as Figure 3a As shown, the method includes:

[0060] S310. Construct an original detection model to be trained based on the single-shot target detector.

[0061] In this embodiment, on the basis of a single target detector, head prediction is added to obtain a constructed original detection model. During the training process, the entire original detection model not only predicts the pedestrian frame, but also predicts the mark of the pedestrian's head, and uses the head detection task to assist in improving the accuracy of pedestrian detection. Among them, the single target detector can be SSD, YOLO, RetinaNet and other detectors.

[0062] Figure 3b is a schematic diagram of the network architecture of an original detection model provided in Embodiment 3 of...

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Abstract

The embodiment of the invention discloses a pedestrian detection method and device, equipment and a medium. The method comprises the steps of acquiring a to-be-detected image; inputting the to-be-detected image into the trained single target detector, and obtaining output information of the single target detector; determining a pedestrian detection result of the to-be-detected image according to the output information of the single target detector; wherein the single-time target detector is obtained by training an original detection model comprising the initially constructed single-time targetdetector and the head detection network in advance. According to the pedestrian detection method provided by the embodiment of the invention, an original detection model comprising an initially constructed single target detector and a head detection network is used in advance; and pedestrian detection is performed on the single target detector obtained by training, thereby improving pedestrian detection precision on the basis of ensuring pedestrian detection speed of the single target detector.

Description

technical field [0001] Embodiments of the present invention relate to the field of target detection, and in particular, to a pedestrian detection method, device, equipment, and medium. Background technique [0002] Pedestrian detection has many application scenarios in the field of computer vision, such as security monitoring, automatic driving, robots, etc. Most of the current mainstream pedestrian detection methods are based on deep learning, such as the target detector Faster RCNN based on the candidate area, or the single target detector SSD, YOLO, etc. The target detector based on the candidate region is divided into two parts, one is the region candidate Region Proposal Networks (RPN) network, and the other is the region-based convolution (FastR-CNN) network. When in use, the RPN roughly extracts the candidate area of ​​the foreground frame, and then the Fast R-CNN fine-tunes the candidate area, and returns the final object coordinates and object classification result...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/103G06F18/214
Inventor 马事伟吴江旭胡淼枫王璟璟聂铭君刘永文戚龙雨石金玉徐达炜张然赵旭民
Owner BEIJING WODONG TIANJUN INFORMATION TECH CO LTD