Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreg...Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation,the detection results in many false detections for the highly dynamic background.To solve these problems,an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper.Firstly,with the pixel’s temporal and spatial information,the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence.Secondly,the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background,to acquire the adaptive segmentation threshold.Thirdly,the update rate is adjusted based on the complexity of the background.Finally,the detected result goes through a post-processing to achieve better detection results.The experimental results show that the improved algorithm will not only quickly suppress the“ghost”,but also have a better detection in a complex dynamic background.展开更多
Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation...Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.展开更多
基金Project(61701060)supported by the National Natural Science Foundation of China。
文摘Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation,the detection results in many false detections for the highly dynamic background.To solve these problems,an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper.Firstly,with the pixel’s temporal and spatial information,the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence.Secondly,the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background,to acquire the adaptive segmentation threshold.Thirdly,the update rate is adjusted based on the complexity of the background.Finally,the detected result goes through a post-processing to achieve better detection results.The experimental results show that the improved algorithm will not only quickly suppress the“ghost”,but also have a better detection in a complex dynamic background.
基金Project (Nos 60602012 and 60675023) supported by the National Natural Science Foundation of Chinathe National High-Tech Re-search and Development Program (863) of China (No 2007AA01Z 164)the Shanghai Key Laboratory Opening Plan Grant (No.06dz22103),China
文摘Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.
