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Object Detection and Tracking for Night Surveillance based on Salient Contrast Analysis
Liangsheng Wang; Kaiqi Huang; Yongzhen Huang; Tieniu Tan
2009
Conference NameInternational Conference on Image Processing
Source PublicationIEEE International Conference on Image Processing, 2009
Pages1113-1116
Conference Date2009
Conference PlaceCairo, Egypt
AbstractNight 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.
KeywordObject Detection   target Tracking   video Signal Processing
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12702
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Liangsheng Wang,Kaiqi Huang,Yongzhen Huang,et al. Object Detection and Tracking for Night Surveillance based on Salient Contrast Analysis[C],2009:1113-1116.
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