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Abstract

In order to improve the shortcomings of the existing algorithm and aiming at the problems of classical algorithm in moving object detection and tracking, a moving object detection algorithm combined with improved texture and chromatic information is proposed. The texture and color two kinds of features insensitive to shadow are used to describe the background, and then the background description is applied to the Gauss mixture model to put forward a new method for moving target detection, which can better resist the influence of shadow and background illumination changes. At the same time, the Camshift tracking algorithm jointing LBP texture and color information and target tracking algorithm based on Kalman filter and Blob matching method are put forward, and the actual effect of the algorithm is tested. The test results showed that the algorithm reduces the computational complexity and improves the robustness of target tracking under the complex scene. It summed up that the tracking algorithm has good performance.

Keywords

Intelligent video surveillance dynamic tracking local two-element mode filter

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Dynamic tracking algorithm suitable for intelligent video surveillance

Abstract

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References