Instituto de Sistemas e Robótica, Instituto Superior Técnico, 1049-001 Lisboa, Portugal.
IEEE Trans Image Process. 2010 May;19(5):1338-48. doi: 10.1109/TIP.2009.2039664. Epub 2009 Dec 31.
This paper proposes an approach for recognizing human activities (more specifically, pedestrian trajectories) in video sequences, in a surveillance context. A system for automatic processing of video information for surveillance purposes should be capable of detecting, recognizing, and collecting statistics of human activity, reducing human intervention as much as possible. In the method described in this paper, human trajectories are modeled as a concatenation of segments produced by a set of low level dynamical models. These low level models are estimated in an unsupervised fashion, based on a finite mixture formulation, using the expectation-maximization (EM) algorithm; the number of models is automatically obtained using a minimum message length (MML) criterion. This leads to a parsimonious set of models tuned to the complexity of the scene. We describe the switching among the low-level dynamic models by a hidden Markov chain; thus, the complete model is termed a switched dynamical hidden Markov model (SD-HMM). The performance of the proposed method is illustrated with real data from two different scenarios: a shopping center and a university campus. A set of human activities in both scenarios is successfully recognized by the proposed system. These experiments show the ability of our approach to properly describe trajectories with sudden changes.
本文提出了一种在监控环境中识别视频序列中人类活动（更具体地说，是行人轨迹）的方法。用于监控目的的视频信息自动处理系统应该能够检测、识别和统计人类活动，尽可能减少人为干预。在本文描述的方法中，人类轨迹被建模为通过一组低级动态模型生成的片段的串联。这些低级模型是基于有限混合公式，使用期望最大化（EM）算法以无监督的方式进行估计的；模型的数量是使用最小信息长度（MML）准则自动获得的。这导致了一组针对场景复杂度进行调整的简洁模型。我们通过隐马尔可夫链来描述低级动态模型之间的切换；因此，完整的模型被称为切换动态隐马尔可夫模型（SD-HMM）。所提出的方法的性能通过来自两个不同场景的真实数据来说明：购物中心和大学校园。提出的系统成功地识别了这两个场景中的一系列人类活动。这些实验表明，我们的方法能够正确描述具有突然变化的轨迹。
