Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):56-71. doi: 10.1109/TPAMI.2008.241.
We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. Then, it accomplishes the following tasks: 1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; 2) modeling paths commonly taken by objects across multiple camera views; and 3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learned, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22,951 and 14,985 trajectories, respectively.
我们提出了一种新的方法，用于分析多个未校准的静态相机视图中的活动。在本文中，我们将活动视为物体的运动模式，对应于远场场景中的路径。我们假设相机的拓扑结构未知且非常任意，这些相机的视场可能没有重叠或有任意量的重叠，并且物体可能在不同的地面平面上移动。使用低级线索，首先在每个相机视图中独立跟踪物体，并且计算物体沿轨迹的位置和速度作为特征。在概率模型下，我们的方法共同学习不同相机视图的特征空间中活动的分布。然后，它完成以下任务：1）将属于同一活动但可能位于不同相机视图中的轨迹分组到一个簇中；2）对物体在多个相机视图中共同走过的路径进行建模；3）检测异常活动。这种方法的优点是它不需要首先解决具有挑战性的对应问题，并且学习是无监督的。即使对应关系不是前提条件，在学习了活动模型之后，它们也可以帮助解决对应问题，因为如果两个来自不同相机视图的轨迹属于同一活动，则它们很可能对应于同一物体。我们的方法在模拟数据集和两个非常大的真实数据集上进行了评估，分别有 22951 和 14985 条轨迹。
