中文摘要：

对于人体行为识别,以前方法提取的轨迹包含了背景的无关的运动变化。同时,由于相机运动,轨迹位移的方向幅度描述符缺乏鲁棒性。针对这些问题,该文提出了跟踪显著相对运动点的行为识别方法。首先利用运动边缘检测器提取运动特征,经自适应门限处理后,将包含显著特征的超像素作为相对运动区域。然后跟踪相对运动超像素内的兴趣点来产生轨迹。对于轨迹形状,预定义的多重方向模式被用来产生轨迹位移矢量的方向分布统计。最后,分别采用轨迹的方向梯度、运动边缘、方向统计及其组合作为分类器的输入来识别行为。在KTH和UCF-sports行为数据库上,提取的相对运动点轨迹能够描述对象的运动变化,方向统计描述符提高了轨迹形状特征的鲁棒性。与相关文献比较,我们方法获得了较好的识别性能。

英文摘要：

The trajectories for action recognition that were extracted by previous methods contained irrelevant motion changes of background,and the Orientation-Magnitude descriptor of trajectory shapes lacked the robustness due to camera movement. To solve these problems,action recognition by tracking the salient relative motion points was proposed in this paper.Firstly,motion boundary detector which suppressed the camera constant motion was utilized to extract motion features. After processing them by the adaptive threshold,super-pixels which contained salient motion boundaries were defined as relative motion regions. Then a method to track the interest points within relative motion super-pixels was employed to generate trajectories. For the trajectory shape,the pre-defined multiple directional patterns were used to produce distribution statistics of direction of trajectory points. Finally,the descriptors like oriented gradient,motion boundary,oriented statistic and their combined representation were fed into the classifier for recognizing actions,respectively. On KTH and UCF-sports datasets,the extracted trajectories can describe the motion changes of objects,and the directional statistics boosts the robustness to the trajectory shape. Compared with the related literature,our method obtains good recognition performance.