A Multi-target Tracking Method Based on Semantic Information and Scene Information

Abstract

The invention discloses a multi-target tracking method based on semantic information and scene information, comprising: separately predicting the position of each tracking target in the scene in the current frame, using a detection model to correct the predicted position and obtaining its semantic score; Use the corrected position as the target frame to obtain the similarity between the target frame and the historical trajectory of the corresponding tracking target, and fuse the semantic score and similarity to obtain the tracking score of the target frame; update the scene network according to the tracking score of the target frame grid scene model, calculate the scene confidence of the target frame according to the scene model, and update the tracking score of the target frame according to the scene confidence; use the detection model to obtain the detection result of the current frame, match the target frame with the detection result, and according to the matching The result and the tracking score of the target box determine the state of the tracked target or generate a new target to get the tracking result of the current frame. The invention can improve the robustness and accuracy of multi-target tracking.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically relates to a multi-target tracking method based on semantic information and scene information. Background technique [0002] Multi-target tracking is an important task in the field of computer vision, and has important application value in automatic driving, robot navigation and motion analysis. The goal of multi-object tracking is to estimate the position of all tracked objects in the scene in each frame of the image and keep the id of the same object unchanged to generate object trajectories. The existing multi-target tracking algorithms can be divided into two categories: offline algorithms and online algorithms. In offline algorithms, the multi-target tracking task is usually described as an optimization problem. By establishing a simplified model such as a network flow model, a k-part graph model or a graph cut model, an optimization algorithm is used to find t...

AI Technical Summary

Problems solved by technology

However, the existing online multi-target tracking algorithms only mechanically combine detection algorithms and single-target tracking algorithms. In fact, detection and tracking are processed separately, which will bring two problems: (1) the detection process is only for a single Image, without using the sequence information of the video, is prone to track discontinuity; (2) the tracking process does not use the semantic information used in detection, and is prone to tracking drift

Due to these two problems, existing multi-target tracking algorithms cannot overcome problems such as frequent occlusions, complex scenes, and camera motions.

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