Compressed irregular block LBP-based video target tracking method

Abstract

The invention discloses a compressed irregular block LBP-based video target tracking method. The method comprises the steps of expressing a tracked target or a candidate target by adopting a compressed and sampled irregular block LBP eigenvector; searching for the candidate target by adopting a particle filtering framework; and judging whether the compressed eigenvector of the candidate target is a target tracking result or not by adopting a Navie Bayesian Classifier. According to the method, the processing speed of video target tracking is increased, and an accurate tracking effect can be kept in various complex scenes of severe illumination change and posture change, view angle rotation and sudden movement, background disorder, similar target interference and the like.

Description

technical field [0001] The invention relates to the field of moving target tracking in video frame sequences, in particular to a video target tracking method based on compressed irregular block LBP. Background technique [0002] Video target tracking refers to analyzing the motion parameters and trajectories (such as position, size, shape, speed, acceleration, etc.) of a specific target from a video sequence with the help of target features (such as color, texture, shape, etc.), which is the core of the computer vision system. One of the tasks, it has broad application prospects in many fields such as intelligent video surveillance, human-computer interaction, medical diagnosis, and robot navigation. However, various complex scene factors such as illumination changes, shadows, occlusions, sudden changes in motion, and background chaos have brought great challenges to video object tracking technology. Accurate and fast video object tracking methods have attracted much attenti...

AI Technical Summary

Problems solved by technology

Therefore, the disadvantages of the MB-LBP feature mainly include the following two: ①A MB-LBP image area cannot involve all possible scale image areas; ②Compute all possible MB-LBP features of a tracking target area to form a high-dimensional MB-LBP feature Vectors are time consuming

[0035] (2) Inadequacies of compressing Haar-like feature vectors

However, many literatures have confirmed that the performance of ③Haar-like features is far inferior to that of MB-LBP features in video target tracking applications in scenarios such as illumination changes, scale changes, and target rotations.

The FCT method assumes that the Haar-like feature vector dimension n=10 6 ~10 10 , ④ cannot accurately express the dimension of the feature vector of the tracked target area

The sparsity of the measurement matrix R is too high, that is, the number of non-zero elements on each row vector is too small, resulting in the loss of the information of ⑤ compressing the Haar-like feature vector

[0037] (3) Inadequacies of coarse-to-fine search strategy

Compared with the commonly used particle filter search strategy, ⑥The candidate targets generated by the coarse-to-fine search strategy in a large number of impossible directions will cause unnecessary computational overhead

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