Multi-target tracking method in video surveillance

Abstract

The invention discloses a video target tracking method capable of integrating ASIFT features and particle filter, and belongs to the technical field of video information processing and mode recognition. The video target tracking method comprises the following steps: the adjacent frame difference method is utilized to obtain moving objects in a video sequence; according to the area corresponding to an acquired complete target, a tracking target model is established; ASIFT feature vectors of the target model are established; the particle filter technology is adopted to predict a candidate area target; ASIFT feature vectors of a candidate target model are established; the feature vectors of the tracking targets are matched with the feature vectors of the candidate target; the RANSAC algorithm is adopted to reject wrong matching; the target model is renewed, so as to realize target tracking. The video target tracking method provided by the invention can accurately and quickly track the targets under the condition that brightness changes and is shielded. Therefore, the multi-target tracking method in the video surveillance has relatively good real-time performance and robustness.

Description

Technical field [0001] The invention belongs to the technical field of video information processing and pattern recognition, in particular to a multi-target tracking method in video surveillance. Background technique [0002] Target tracking has always been the foundation of machine vision, artificial intelligence and pattern recognition. Target tracking can be widely used in industries such as navigation and positioning, military guidance, and security monitoring. [0003] Target tracking is to use the known target location information and target continuity to find a moving target of interest on a sequence of images. At present, there are many target tracking methods in video surveillance, such as tracking methods based on particle filtering, tracking methods based on Mean Shift, and target tracking methods based on Kalman filtering. However, in these traditional methods, when the target is occluded and there is interference in the surrounding environment (such as noise, light),...

AI Technical Summary

Problems solved by technology

However, this technology does not have high anti-affine performance, and there are also deficiencies in target matching accuracy. For targets with large deformations, target loss is prone to occur.

Furthermore, this method also has defects in real-time

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