Multi-target tracking method based on depth track prediction

Abstract

The invention discloses a multi-target tracking method based on depth track prediction. The method comprises the following steps: constructing a track prediction model based on a long-short time memory network for a multi-target tracking system; using the trajectory data of the real tracking scene to train a trajectory prediction model; constructing conservative short-time trajectory fragments byusing the appearance characteristics of target detection, and calculating the appearance similarity among the trajectory fragments; carrying out depth track prediction on the target on line by using the trained track prediction model, obtaining the motion similarity between track segments, comprehensively considering the appearance similarity and the motion similarity, and setting a network modelof target tracking to complete multi-target tracking. According to the method, a long-short time memory network-based trajectory prediction model is constructed for a multi-target tracking system, andcompared with a traditional method, the method can fully consider the historical trajectory information and scene information of the target, calculate the inter-target motion similarity with better robustness, and further improve the multi-target tracking effect.

Description

technical field [0001] The invention relates to the technical fields of computer vision and deep learning, in particular to a multi-target tracking method based on depth trajectory prediction. Background technique [0002] Multi-target tracking is a research hotspot in the field of computer vision, and it has a wide range of applications in real life, such as video surveillance, sports event analysis, biological research, human-computer interaction, robot navigation, unmanned driving, etc. According to different target initialization methods, most current multi-target tracking algorithms can be divided into Detection-Based Tracking (DBT) and Detection-Free Tracking (DFT). The detection-based tracking algorithm It is becoming more and more popular. [0003] Despite decades of hard work by scholars, the multi-target tracking task is still far from reaching human-like accuracy, mainly because of several very difficult problems in the multi-target tracking task: the number of t...

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Problems solved by technology

[0003] Despite decades of hard work by scholars, the multi-target tracking task is still far from reaching human-like accuracy, mainly because of several very difficult problems in the multi-target tracking task: the number of targets is uncertain and dynamically changing, Frequent occlusion between targets, similar appearance between targets, complex motion of targets, possible interactions between targets, etc.

In the existing multi-target tracking algorithms, the appearance similarity and motion similarity between targets are often used for data association. In the calculation of motion similarity, most methods usually describe the motion characteristics of the target as linear motion or a specific nonlinear motion, it is difficult to accurately describe the complex motion of the target in the real world

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