Abstract: Aiming at the disadvantageous affects caused by moving object detection and tracking in complex background of video scenes,a new method of detecting and tracking specific moving objects using adaptive moving energy threshold combined with compact colored SIFT descriptor is proposed. For detection of moving objects,disturbance of complex environment is filtered out automatically by adaptive moving energy threshold. Principal Components Analysis is applied to the Colored SIFT descriptor(PCA-CSIFT) for objects matching. Thereby the continuous tracking of specific moving objects is achieved. Extensive experiments on bench datasets show that,in complex background,the moving objects tracking method is not sensitive to the amount of objects and the ratio of error is stabilized at 6. 5% ~ 34% . The PCA-CSIFT holds high distinctiveness and robustness with ratio of mismatches 25. 33% ~28% . The average processing time of each frame is no more than 0. 26 s,so the method meets the need of real time.

Key words: moving objects detection, moving objects tracking, adaptive moving energy threshold, complex background, objects matching

摘要： 针对复杂环境对运动目标检测与跟踪产生的不利影响,提出一种自适应运动能量阈值结合精简彩色SIFT 描述子的特定运动目标检测与跟踪方法。运用自适应运动能量阈值方法自动滤除复杂环境干扰以完成运动目标 检测,由此形成目标匹配搜索域,并给出经主成份分析及精简后的彩色SIFT 描述子(PCA-CSIFT)进行目标匹配, 从而实现特定运动目标的连续跟踪。实验结果表明,在复杂环境下,运动目标检测方法对目标总量变化不敏感,错 误率始终稳定在6. 5% ~34% 之间。PCA-CSIFT 算法在保持高可区分性的同时错误匹配率为25. 33% ~ 28% ,平 均每帧处理时间不超过0. 26 s,具有较好的鲁棒性与实时性。

关键词: 运动目标检测, 运动目标跟踪, 自适应运动能量阈值, 复杂背景, 目标匹配

CLC Number: 

• TP391