Target tracking method oriented to video with low frame rate

Abstract

The invention discloses a target tracking method oriented to a video with a low frame rate. The method comprises the following steps: (1), representing a target region by a method integrating a dominant color and a space distribution characteristic thereof; (2), employing a cross color ratio-based matching criterion to carry out similarity matching on a candidate region and the target region; (3), employing a parameter integrogram-based fitness function to characterizing a matching degree of a sample particle and a target template; and (4), utilizing an annealing particle swarm optimization framework with simulation of biological swarm intelligence to search abrupt motions caused by a video with a low frame rate. According to the invention, the effective target tracking method is realized; and moreover, experimental results show that the provided method, compared with other classical low frame rate tracking methods, has good effectiveness and robustness.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an object tracking method for low frame rate video. Background technique [0002] In many practical machine vision applications, available resources are limited (for example: embedded vision systems). Therefore, some low frame rate (Low Frame Rate) videos will be produced in the process of video acquisition. The specific reasons are as follows: 1) due to the delay of video acquisition hardware or the limitation of transmission bandwidth, the Frame drop during transmission; 2) Due to the limitation of CPU processing power or storage capacity, the collected video data is down-sampled in the time dimension, thereby increasing the processing rate or reducing the storage capacity. [0003] Since the low frame rate video data displays less than 10 frames of image data per second, the appearance or motion of the target object in its continuous image frames has a large mutation. However,...

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Problems solved by technology

[0004] At home and abroad, there are few researches directly on target tracking under low frame rate video. Among them, Porikli et al. have expanded the mean shift algorithm, and adopted a multi-core mean shift algorithm for significant motion areas on the basis of background subtraction, thus overcoming the problem caused by Motion discontinuities and unpredictability issues caused by low frame rates

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