Multi-camera multi-target perceptual activity recognition via meta-data fusion (Conference Presentation)

A. Shirkhodaie, Kalyankumar Bogi
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Abstract

Human activity detection and recognition capabilities have broad applications for civilian, military, and homeland security. However, monitoring of human activities are very complicated and tedious tasks especially when multiple persons involved perform activities in confined spaces that impose significant obstruction, occultation and observability uncertainty. These applications require fast and reliable tracking systems to observe and inference dynamic objects from multiple coherent video sequences. In compact surveillance systems utilization of multi-cameras monitoring system is highly imperative for tracking, inference, and recognition of variety of group activities. With multi-cameras systems, complexity of occultation can be dealt with by finding and correlating the correspondences from within multiple cameras views observing the same target at once. In this paper, we demonstrate one such a multi-person tracking system developed in a virtual environment. By example, we demonstrate an efficient and effective technique for multi-target tracking, discrimination, and activity recognition in confined spaces. The exemplary scenario considered under this study represents a bus activity where multiple passengers arrive, take seats, and leave while being monitoring by four concurrently operating surveillance camera systems. In this paper, we present how processing tasks of multiple cameras are shared, what objects features they detect, track, and identify jointly. Furthermore, we present the computational intelligence techniques for processing multi-camera images for recognition of objects of interest as well as for annotation of observed individual and group activities via meta-data imagery fusion. The proposed multi-camera processing system is shown to have efficiency and effectively to track multiple targets with different degree of social interactions either with one another or with objects involved with their activities.
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基于元数据融合的多相机多目标感知活动识别(会议报告)
人类活动检测和识别能力在民用、军事和国土安全方面有着广泛的应用。然而,监测人类活动是一项非常复杂和繁琐的任务,特别是当多人在密闭空间中进行活动时,会造成严重的障碍物、遮挡和可观测性不确定性。这些应用需要快速可靠的跟踪系统来观察和推断来自多个相干视频序列的动态对象。在紧凑型监控系统中,利用多摄像机监控系统对各种群体活动进行跟踪、推断和识别是非常必要的。在多相机系统中,可以通过查找和关联同时观测同一目标的多个相机视图中的对应关系来处理掩星的复杂性。在本文中,我们展示了一个在虚拟环境中开发的多人跟踪系统。通过实例,我们展示了一种在密闭空间中进行多目标跟踪、识别和活动识别的高效技术。本研究考虑的示例场景是在四个同时运行的监控摄像头系统的监控下,多名乘客到达、就座和离开的公共汽车活动。在本文中,我们提出了如何共享多个摄像机的处理任务,以及它们共同检测、跟踪和识别的对象特征。此外,我们提出了用于处理多摄像头图像的计算智能技术,以识别感兴趣的对象,以及通过元数据图像融合对观察到的个人和群体活动进行注释。实验结果表明,该多摄像机处理系统能够有效地跟踪具有不同社会互动程度的多个目标,无论是彼此之间还是与其活动相关的物体之间。
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