Ordered Trajectories for Large Scale Human Action Recognition
Ordered Trajectories for Large Scale Human Action Recognition
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DOI：
10.1109/iccvw.2013.61
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
O. V. R. Murthy;Roland Göcke
中科院分区：
文献类型：
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作者：
O. V. R. Murthy;Roland Göcke
最近，基于密集轨迹的视频表示已被证明在几个基准数据集上优于其他人类动作识别方法。在密集轨迹中，点在空间和时间上以均匀的间隔采样，然后使用密集光流场进行跟踪。均匀采样不会将感兴趣的对象与背景或其他对象区分开。因此，积累了大量信息，但实际上可能没有用。有时，如果这些不需要的信息的内容比感兴趣的主要对象的信息大得多，则这些不需要的信息可能会使学习过程产生偏差。当由于行动类别的数量增加或在不同的空间和时间尺度上计算密集的轨迹而积累越来越多的数据时，这种情况尤其会升级，如时空金字塔方法。相比之下，我们提出了一种技术，只选择了几个密集的轨迹，然后生成一组新的轨迹被称为“有序的轨迹”。我们评估我们的技术在复杂的基准HMDB51，UCF50和UCF101数据集包含50个或更多的动作类，并观察到提高性能的识别率和以较低的计算成本去除背景杂波。
Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field. The uniform sampling does not discriminate objects of interest from the background or other objects. Consequently, a lot of information is accumulated, which actually may not be useful. Sometimes, this unwanted information may bias the learning process if its content is much larger than the information of the principal object(s) of interest. This can especially escalate when more and more data is accumulated due to an increase in the number of action classes or the computation of dense trajectories at different scales in space and time, as in the Spatio-Temporal Pyramidal approach. In contrast, we propose a technique that selects only a few dense trajectories and then generates a new set of trajectories termed 'ordered trajectories'. We evaluate our technique on the complex benchmark HMDB51, UCF50 and UCF101 datasets containing 50 or more action classes and observe improved performance in terms of recognition rates and removal of background clutter at a lower computational cost.