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A Deep Learning-Based Multi-Pedestrian Tracking Method

A technology of deep learning and pedestrian tracking, which is applied in the field of multi-pedestrian tracking based on deep learning, can solve the problems of trajectory drift and occlusion problem processing ability, so as to improve robustness, avoid the deviation of manual selection features, and improve research value Effect

Active Publication Date: 2022-07-26
SICHUAN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional methods are prone to trajectory drift problems in complex scenes with many targets, and the ability to deal with occlusion problems is also limited.

Method used

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  • A Deep Learning-Based Multi-Pedestrian Tracking Method
  • A Deep Learning-Based Multi-Pedestrian Tracking Method
  • A Deep Learning-Based Multi-Pedestrian Tracking Method

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Embodiment Construction

[0030] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It is necessary to point out that the following embodiments are only used to further illustrate the present invention, and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art are familiar with the present invention. According to the above-mentioned content of the invention, some non-essential improvements and adjustments are made to the present invention for specific implementation, which should still belong to the protection scope of the present invention.

[0031] figure 1 , multi-pedestrian tracking based on deep learning, including the following steps:

[0032] (1) Train the fused appearance feature network of the tracker in a supervised learning method: For different branches, use the cosine-softmax loss function to train separately, and then fuse the features of each branch in seri...

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Abstract

The invention provides an online multi-pedestrian tracking method based on deep learning, which mainly involves tracking multi-pedestrians in complex scenes by means of deep learning. The method includes: proposing a multi-layer feature fusion network to extract the appearance features of the target. Use a high-precision object detector to detect the position of the object in the video, and extract the appearance features of the object. For different state trajectories, considering the confidence of appearance features and motion features, the difference metric matrix is ​​calculated in different ways. According to the confidence of different state trajectories, combined with the metric matrix, the Hungarian algorithm is used to realize the hierarchical data association of detection and trajectories. Finally, the trajectory information is updated according to the association result. The invention fully considers the confidence of the target motion feature and the appearance feature, and reasonably utilizes the two features to calculate the difference metric matrix. At the same time, the data association method of hierarchical association can better deal with the problems of pedestrian false detection and occlusion, and effectively reduce the number of trajectory ID transformations.

Description

technical field [0001] The invention relates to the multi-target tracking problem in the field of deep learning, in particular to a multi-pedestrian tracking method based on deep learning. Background technique [0002] Video-based tracking algorithms have always been an important branch of computer vision research, and emerging new technologies such as correlation filtering, convolutional neural networks, etc. have also been rapidly integrated into the field and significantly improved the average level of the field. With the emergence of a large number of deep learning technologies, the field of computer vision has once again achieved rapid development. The tracking algorithms based on deep learning are constantly improving and have a wide range of application scenarios. For example: intelligent video monitoring and control, abnormal behavior identification and analysis, human-computer interaction, medical image analysis, pedestrian flow analysis in public places, etc. [0...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246G06T7/277G06V10/80G06V10/82
CPCG06T7/246G06T7/277G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30196G06T2207/30241G06V2201/07G06F18/253
Inventor 卿粼波牛通何小海许盛宇吴晓红苏婕
Owner SICHUAN UNIV
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