提出一种基于控制点的分层双向动态规划立体匹配算法.首先,利用改进Volum etric迭代算法获取具有高可靠度的控制点,将其作为具有正确视差的匹配点.其次,在高可靠度控制点的指导下,利用分层双向动态规划算法在DSI(d isparity-space im a...提出一种基于控制点的分层双向动态规划立体匹配算法.首先,利用改进Volum etric迭代算法获取具有高可靠度的控制点,将其作为具有正确视差的匹配点.其次,在高可靠度控制点的指导下,利用分层双向动态规划算法在DSI(d isparity-space im age)视差空间图中进行初匹配,进而在Delta DSI(delta d isparity-space im age)视差变化空间图中进行精匹配,从而获取高密度视差图.实验结果表明,该算法不仅可以改善传统直接动态规划立体匹配算法产生的带状条纹瑕疵,而且计算速度较快,匹配结果也优于传统动态规划的匹配结果.*展开更多
An adaptive human tracking method across spatially separated surveillance cameras with non-overlapping fields of views （FOVs） is proposed. The method relies on the two cues of the human appearance model and spatio-t...An adaptive human tracking method across spatially separated surveillance cameras with non-overlapping fields of views （FOVs） is proposed. The method relies on the two cues of the human appearance model and spatio-temporal information between cameras. For the human appearance model, an HSV color histogram is extracted from different human body parts （head, torso, and legs）, then a weighted algorithm is used to compute the similarity distance of two people. Finally, a similarity sorting algorithm with two thresholds is exploited to find the correspondence. The spatio- temporal information is established in the learning phase and is updated incrementally according to the latest correspondence. The experimental results prove that the proposed human tracking method is effective without requiring camera calibration and it becomes more accurate over time as new observations are accumulated.展开更多
文摘提出一种基于控制点的分层双向动态规划立体匹配算法.首先,利用改进Volum etric迭代算法获取具有高可靠度的控制点,将其作为具有正确视差的匹配点.其次,在高可靠度控制点的指导下,利用分层双向动态规划算法在DSI(d isparity-space im age)视差空间图中进行初匹配,进而在Delta DSI(delta d isparity-space im age)视差变化空间图中进行精匹配,从而获取高密度视差图.实验结果表明,该算法不仅可以改善传统直接动态规划立体匹配算法产生的带状条纹瑕疵,而且计算速度较快,匹配结果也优于传统动态规划的匹配结果.*
基金The National Natural Science Foundation of China(No. 60972001 )the Science and Technology Plan of Suzhou City(No. SG201076)
文摘An adaptive human tracking method across spatially separated surveillance cameras with non-overlapping fields of views （FOVs） is proposed. The method relies on the two cues of the human appearance model and spatio-temporal information between cameras. For the human appearance model, an HSV color histogram is extracted from different human body parts （head, torso, and legs）, then a weighted algorithm is used to compute the similarity distance of two people. Finally, a similarity sorting algorithm with two thresholds is exploited to find the correspondence. The spatio- temporal information is established in the learning phase and is updated incrementally according to the latest correspondence. The experimental results prove that the proposed human tracking method is effective without requiring camera calibration and it becomes more accurate over time as new observations are accumulated.
