Felzenszwalb-Baum-Welch：通过改变外观进行事件检测
Felzenszwalb-Baum-Welch: Event Detection by Changing Appearance
摘要：
我们提出了一种方法，它可以检测视频中的事件，通过建模的事件参与者随着时间的推移外观的变化。这种方法可以检测出不是以运动为特征，而是以所涉及的人或物体的变化状态为特征的事件。这是通过使用对象检测器作为隐马尔可夫模型（HMM）的状态的输出模型来实现的。该方法允许HMM对事件参与者随时间的姿态序列进行建模，并且对于人类和无生命物体的姿态是有效的。将现有的对象检测方法作为事件模型的一部分使用的能力使得利用对象检测社区正在进行的工作成为可能。一种新的训练方法使用EM循环来同时自动学习时间结构和对象模型，而不需要指定要建模的各个姿势或它们发生的帧。E步骤估计视频帧到HMM状态的潜在分配，而M步骤估计HMM转移概率和状态输出模型，包括对象检测器，其在分配给其状态的帧的加权子集上进行训练。收集了一个新的数据集，因为对以改变对象姿势为特征的事件做了很少的工作，并且没有合适的数据集。我们的方法产生的结果优于该数据集上的比较系统。
英文摘要：
We propose a method which can detect events in videos by modeling the change in appearance of the event participants over time. This method makes it possible to detect events which are characterized not by motion, but by the changing state of the people or objects involved. This is accomplished by using object detectors as output models for the states of a hidden Markov model (HMM). The method allows an HMM to model the sequence of poses of the event participants over time, and is effective for poses of humans and inanimate objects. The ability to use existing object-detection methods as part of an event model makes it possible to leverage ongoing work in the object-detection community. A novel training method uses an EM loop to simultaneously learn the temporal structure and object models automatically, without the need to specify either the individual poses to be modeled or the frames in which they occur. The E-step estimates the latent assignment of video frames to HMM states, while the M-step estimates both the HMM transition probabilities and state output models, including the object detectors, which are trained on the weighted subset of frames assigned to their state. A new dataset was gathered because little work has been done on events characterized by changing object pose, and suitable datasets are not available. Our method produced results superior to that of comparison systems on this dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
