Temporal Information Cooperated Gaussian Mixture Models for Real-time Surveillance with Ghost Detection
Tianci Huang, Chengjiao Guo, Jingbang Qiu, Takeshi Ikenaga · 2009
This paper describes a new real-time approach for detecting motions in the video streams taken from stationary cameras. This method combines a temporal recording scheme with the adaptive background model subtraction scheme. To save the computation brought from conventional Gaussian Mixture Models (GMM) and achieve real-time processing, an adaptively adjusted mechanism is proposed. On the other hand, illumination changes, shadow influence, and ghost in scene, these three important problems which result in low segmentation quality are settled down by utilizing proposed features and temporal information from video streams. The experimental results validate the improvement of detection accuracy. Meanwhile, the execution time for each component per frame is calculated and compared with that of conventional Gaussian Mixture Models.