Video target tracking method based on CAMSHIFT and Kalman filtering

Abstract

The invention discloses a video target tracking method based on CAMSHIFT and Kalman filtering. The method takes a search window as a parameter and comprises the following steps of: predicting a search window Win by a Kalman filter; calling a CAMSHIFT algorithm by taking Win as a parameter to search for the target, and returning to the window targetWin containing candidate models; calculating similarity between the targetWin and the target model; if the similarity is greater than the set threshold, finding the target and returning to the target window; otherwise, taking the target window of the previous frame and the distance predicted by the Kalman filter as a window expanding parameter instead of enlarge, and calling an adaptive iterative search algorithm. By introducing the similarity judgment and the re-positioning technology, the method effectively eliminates the background interference and improves the tracking precision.

Description

technical field [0001] The invention relates to the technical field of video monitoring, in particular to a video target tracking method based on CAMSHIFT and Kalman filtering. Background technique [0002] With the development of computer vision technology, moving video target tracking technology has become an important topic in the fields of weapon guidance, pattern recognition, and computer vision. Because moving video target tracking technology has broad application prospects in both military and civilian fields, many scholars at home and abroad are engaged in the research of this subject and have proposed many classic target tracking algorithms. Moving video target tracking organically combines image processing, computer vision and information science to form a technology that can automatically identify targets from video images in real time, extract target location information, and automatically track targets. Due to the complexity of the real environment, such as occ...

AI Technical Summary

Problems solved by technology

However, when the target has obvious scale changes, especially when the target size gradually increases beyond the range of the kernel window width, the fixed kernel window width often leads to the loss of the target.

[0005] The CAMSH IFT algorithm, as a continuous adaptive Mean Shift, can effectively solve the problem of target deformation by automatically adjusting the size of the kernel window and the size of the tracked target in the image, but its algorithm also converges to a local maximum, and has no control over the target Judgment of similarity

When the search window of CAMSHIFT contains multiple candidate models with similar features, the CAMSHIFT algorithm cannot distinguish whether there is interference, and the problem of insufficient tracking accuracy often occurs

Moreover, when the target moving object passes obstacles slowly or moves quickly and irregularly, the CAMSHIFT algorithm will easily fail, resulting in the loss of target tracking

At the same time, when the target has a large acceleration or is blocked at an instant, the CAMSHIFT tracking algorithm is prone to failure

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