Multi-target Tracking Method Based on Deep Reinforcement Learning

Abstract

The invention discloses a multi-target tracking method and device based on deep reinforcement learning, wherein the method includes: extracting pedestrian features; inputting pedestrian features into a prediction network to predict pedestrian positions; obtaining pedestrian information according to pedestrian positions, and Input the decision network for judgment to track the target. This method can take advantage of the interactive utilization of information between different targets and environments, greatly improving the accuracy and performance of tracking.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a multi-target tracking method and device based on deep reinforcement learning. Background technique [0002] MOT (Multi-Object Tracking, multi-object tracking technology) has in-depth applications in various aspects such as video surveillance, human-computer interaction, and automatic driving. The purpose of multi-object tracking is to estimate the trajectory of different targets in the video and track them. Although there are many methods about MOT, which have been continuously proposed, it is very difficult to solve this problem in many unconstrained scenes, especially in crowded environments, because of the occlusion and huge intra-class differences between different objects. due to. [0003] In related technologies, multi-target tracking technologies can be mainly divided into two categories, the first one is offline type (also called batch processing type)...

AI Technical Summary

Problems solved by technology

[0004] However, the tracking accuracy of these methods is not very high, mainly because these methods are sensitive to occlusion and noise, such as missed detection, false detection and inaccurate labeling, etc.

Images