Image target tracking method

Abstract

The invention provides an image target tracking method, which comprises the following steps: obtaining a first frame of image, integrating the appearance models of the local image blocks of different scales into a whole image dictionary, and calculating the sparse coefficient of each local image block; collecting the current state of the image as a candidate target, establishing a particle filter and a similarity function, and calculating the estimation position of a candidate target through the similarity function in a current state particle filter frame; and taking the estimation position of the candidate target as a basis to carry out the spare coefficient recalculation of the local image block to finish the final positioning position of the target. The image target tracking method improves a traditional tracking method which carries out the random sampling consistency identification of the target through statistical characteristics, and provides a way that at a target object in a complex background is identified through a distinguishing type model constructed by the spare coefficients of the local image blocks. Through a candidate target similarity function fused by a distinguishing type and a generation type, the stability and the accuracy of the tracking method are improved.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to an image target tracking method in computer image processing. Background technique [0002] With the continuous development of sensor technology, computer hardware processing capability and storage technology in recent years, moving target tracking has become a popular research field in pattern recognition and computer vision, and has a wide range of applications in military and civilian fields. Visual information is one of the most important information that humans obtain from the outside world through the senses. At the same time, video sequences have more effective information than static images. Therefore, the segmentation and tracking of targets in video sequences is the premise and basis of subsequent research work, such as Abnormal behavior detection, target recognition and other tasks are based on the tracking and segmentation of targets. [0003] The s...

AI Technical Summary

Problems solved by technology

[0004] However, in reality, there are a large number of factors that affect the accuracy of moving targets and their shape feature extraction, such as target occlusion, rotation in space, and targets entering and exiting the field of view will affect the performance of most trackers, resulting in gradual accumulation of errors and final tracking. There will be a large deviation in the result; there will be a large number of interferences of similar targets in a crowded scene, which will make the detection and tracking of the target more difficult; many factors that interfere with the extraction of target edge features, such as changes in the posture of moving targets will affect the performance based on edges, gradients, etc. The performance of the feature tracker makes it unable to detect areas similar to the training samples and affects the tracking results; for example, camera shake and shadows will cause grayscale and large changes in the content of two adjacent frames of the video sequence, which will affect most Tracker performance; In addition, such as sudden changes in lighting, weak light, low visibility, or changes in the contrast between the indoor scene target and the background will cause changes in the color characteristics of the target. Due to the sensitivity of visual features to light changes, it will be Affects the results of some trackers based on color features

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