Spherical Vision for Visual Surveillance

最近对智能视频监控的需求和挑战的分析表明，COTS系统缺乏许多有用的视频处理功能，因为学术研究没有为大多数实际应用产生可靠的结果。因此，最先进的商业系统只依赖于基本的图像处理，不能充分支持安全代理。增强安全和态势感知迫切需要的视频处理功能包括人员跟踪（在每个时刻定位人员以进行识别）和理解场景中的活动（检测异常行为或特定动作，如跑步）。这两种功能通常使用标准固定摄像机进行调查，尽管在最近的一些研究中使用了PTZ摄像机。单固定摄像机在监控应用中覆盖了小范围的可见性。通常考虑使用可以覆盖更大区域的相机（PTZ，以及最近的球形）。我们之前对PTZ相机的研究表明，在人员跟踪过程中处理变化的视点是困难的。事实上，人们通常用固定相机从同一个角度观看。当PTZ相机旋转时（视图可以从侧面改变到正面），新的球形相机也不是这样。由于球形相机的特性，一个穿过场景的人可以首先从正面观察，在顶部的某个点，然后从侧面或背面观察。利用球面视觉摄像机，我们旨在更详细地研究视频监控中人物跟踪和识别背景下的视点变化问题。我们对摄像机的最初应用将是在非常大的视点变化下从监视名单中识别个人身份。我们将首先跟踪场景中的人，收集他们的外观信息，然后使用分类器集合逐步将每个人的外观与观察列表进行匹配。在全球范围内，我们的目标是(1)开发强大的跟踪和识别技术来处理球面相机提供的极端视点变化；(2)研究在这些变化的视点下模拟人类活动的策略。

A recent analysis of the needs and challenges of intelligent video surveillance has shown that COTS systems lack many useful video processing features because academic research has not produced reliable results for most real-world applications. Therefore, state-of-the-art commercial systems only rely on basic image processing and do not adequately support security agents. Video processing features urgently needed for enhance security and situation awareness include functions for human tracking (to localize a person at each instant for identification) and understanding the activities in the scene (to detect unusual behaviors or specific actions like running). These two functions are usually investigated using standard fix cameras, although PTZ cameras were used in some recent research. Single fix cameras cover small areas of visibility in surveillance application. The use of cameras that can cover a larger area is often considered (PTZ, and more recently spherical). Our previous research with PTZ camera has shown the difficulty with handling changing viewpoints during person tracking. Indeed, people are usually viewed from the same angle with a fix camera. This is not true with PTZ cameras as they rotate (the view can change from side to frontal), nor the new spherical cameras. Because of the nature of a spherical camera, a person crossing a scene can be at first viewed from the front, at some point from the top, and then from the side or the back. With a spherical vision camera, we aim to study in more detail the issue of viewpoint change in the context of person tracking and recognition in video surveillance. Our initial application for the camera will be the recognition of individuals for person identification from a watchlist under very large viewpoint changes. We will first track the people in the scene to collect information about their appearance, and then progressively match the appearance of each person against the watchlist using ensemble of classifiers. More globally, our objectives are (1) to develop robust tracking and recognition techniques to handle the extreme viewpoint change provided by spherical cameras, and (2) to investigate strategies to model human activities under these changing viewpoints.