An Object Tracking Method Based on Trajectory Entropy Minimization

Abstract

The invention provides an object tracking method for minimizing trajectory entropy, which belongs to the technical fields of computer graphics and images. It specifically includes the following steps: target selection, selecting and determining the target object to be tracked from the initial image. Image input, in the case of real-time processing, extract the video image collected by the camera and stored in the storage area as the input image to be tracked; in the case of offline processing, decompose the captured video file into an image composed of multiple frames Sequence, in time order, extracts frame images one by one as input images. Detectors are created and the number of detectors does not exceed 5. Generate tracking trajectory Each detector detects the target at the same time, the trajectory entropy minimizes the target positioning, and the detector is updated. According to the determined target trajectory, similar to the detector training process in the target trajectory, each frame corresponding to the target trajectory is in the target area and Positive and negative samples are extracted separately from the background region, and all detectors are updated. For video surveillance systems.

Description

technical field [0001] The invention belongs to the technical fields of computer vision, graphics and images. Background technique [0002] Visual object tracking is one of the most important components in computer vision applications, such as intelligent surveillance, human-computer interaction, automatic control systems, etc. The purpose of object tracking is to automatically determine its position and size in each subsequent frame given the position and size of the initial object. Although the research on object tracking has been carried out for decades, and a lot of important progress has been made in recent years, due to the complexity of the real world, such as background interference, appearance and illumination changes, low image quality, frame skipping, etc. Designing tracking methods that can achieve human-level performance is still very difficult. An ideal tracking method must consider the real-time performance, stability and persistence of tracking at the same ...

AI Technical Summary

Problems solved by technology

However, these methods are still difficult to adapt to more complex tracking environments, which provides a possible solution to the analysis and processing of tracking trajectories.

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