Object detection and tracking for night surveillance based on salient contrast analysis

Night surveillance is a challenging task because of low brightness, low contrast, low signal to noise ratio (SNR) and low appearance information. Most existing models for night surveillance share the following problems: a lack of adaptability for different scenes and separation between detection and tracking. To solve these problems we propose a model based on salient contrast change (SCC) feature, which applies learning process to enhance adaptability and analyzes trajectories to improve the effectiveness of detection. Empirical studies on several real night videos show that the proposed model is more effective than the original CC model and other traditional models.