Added: Nov 18, 2008
From: magdimohamed2007
Duration: 0:20
Multiple-target tracking has received tremendous attention due to its wide practical applicability in video processing and analysis applications. Most existing techniques, however, suffer from the well-known "multi-target occlusion" problem and/or immense computational cost due to the use of high-dimensional joint-state representations. In this system, we present a distributed Bayesian framework using multiple collaborative cameras for robust and efficient multiple-target tracking in crowded environments with significant and persistent occlusion. When the targets are in close proximity or present multi-target occlusions in a particular camera view, camera collaboration between different views is activated in order to handle the multi-target occlusion problem in an innovative way. Specifically, we propose to model the camera collaboration likelihood density by using epipolar geometry with sequential Monte Carlo implementation. Experimental results have been demonstrated for both synthetic and real-world video data [Reference1: Wei Qu, Dan Schonfeld, and Magdi Mohamed, "Distributed Bayesian Multiple-Target Tracking in Crowded Environments Using Multiple Collaborative Cameras", in the EURASIP Journal on Applied Signal Processing, Special Issue on Tracking in Video Sequences of Crowded Scenes, 2007, Article ID 38373, 15 pages, doi:10.1155/2007/38383], [Reference2: Wei Qu, Dan Schonfeld, and Magdi Mohamed, "Method and Apparatus to Facilitate Use of Conditional Probabilistic Analysis of Multi-Point-of-Reference Samples of an Item to Disambiguate State Information as Pertains to the Item", filed application (US20080089578-A1) with the United States Patent Office on December 21, 2006].
Channel: Tech
Tags: hidden-markov-model multi-camera-tracking particle-filter surveillance
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