Target detection via statistical model learning
Xian Sun, Songhao Zhu, Dongliang Jin, Zhiwei Liang, Guozheng Xu · 2016
This paper presents a statistical treatment of background modeling for use in target detection, where the global information and local information is added into the statistical framework to construct a robust background model to achieve accurate object detection results. Specifically, a novel self-adaptive Gaussian mixture model is proposed to construct a statistical background model based on the global information, which is utilized to deal with the target detection issue under illumination changes; for the target detection issue under dynamic background, the self-tuning spectral clustering technology is first utilized to cluster the background image, the kernel density estimation method is then utilized to construct a statistical background model based on the local information. Experimental results demonstrate that the proposed algorithm can improve the detection performance under illumination changes or dynamic background.