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Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
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@MISC{Khan_trackingmultiple,
author = {Saad M. Khan and Mubarak Shah},
title = {Tracking Multiple Occluding People by Localizing on Multiple Scene Planes},
year = {}
}
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Abstract
Abstract—Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multiview approach to solve this problem. In our approach, we neither detect nor track objects from any single camera or camera pair; rather, evidence is gathered from all of the cameras into a synergistic framework and detection and tracking results are propagated back to each view. Unlike other multiview approaches that require fully calibrated views, our approach is purely image-based and uses only 2D constructs. To this end, we develop a planar homographic occupancy constraint that fuses foreground likelihood information from multiple views to resolve occlusions and localize people on a reference scene plane. For greater robustness, this process is extended to multiple planes parallel to the reference plane in the framework of plane to plane homologies. Our fusion methodology also models scene clutter using the Schmieder and Weathersby clutter measure, which acts as a confidence prior, to assign higher fusion weight to views with lesser clutter. Detection and tracking are performed simultaneously by graph cuts segmentation of tracks in the space-time occupancy likelihood data. Experimental results with detailed qualitative and quantitative analysis are demonstrated in challenging multiview crowded scenes. Index Terms—Tracking, sensor fusion, graph-theoretic methods. Ç 1
Keyphrases
multiple occluding people multiple scene plane multiview approach planar homographic occupancy constraint track object graph-theoretic method quantitative analysis localize people multiple view graph cut segmentation camera pair cluttered scene reference plane single camera space-time occupancy likelihood data individual people fusion methodology synergistic framework likelihood information sensor fusion tracking result single view abstract occlusion reference scene plane experimental result weathersby clutter measure index term tracking
