TY - JOUR T1 - Object Detection, Tracking and Recognition for Multiple Smart Cameras JF - Proceedings of the IEEE Y1 - 2008 A1 - Sankaranarayanan,A. C A1 - Veeraraghavan,A. A1 - Chellapa, Rama KW - algorithm;geometric KW - analysis;image KW - association;distributed KW - camera;visual KW - cameras; KW - cameras;object KW - colour KW - constraints;imaging KW - data KW - detection;object KW - detection;sensor KW - detection;three-dimiensional KW - fusion;target KW - model;video KW - network;distributed KW - recognition;object KW - scene KW - sensor KW - sensor;multiple KW - sensors;geometry;image KW - sensors;object KW - smart KW - texture;intelligent KW - tracking;target KW - tracking;video AB - Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how real-world constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are (a) the presence of a world plane, (b) the presence of a three-dimiensional scene model, (c) consistency of motion across cameras, and (d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed. VL - 96 SN - 0018-9219 CP - 10 M3 - 10.1109/JPROC.2008.928758 ER -