A Small Moving Object Detection Algorithm Based on Track in Video Surveillance

To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.