IEEE Trans Pattern Anal Mach Intell - Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera.

Tópicos

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Resumo

In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.

Resumo Limpo

paper address problem automat detect track variabl number person complex scene use monocular potenti move uncalibr camera propos novel approach multiperson trackingbydetect particl filter framework addit final highconfid detect algorithm use continu confid pedestrian detector onlinetrain instancespecif classifi grade observ model thus generic object categori knowledg complement instancespecif inform main contribut paper explor unreli inform sourc can use robust multiperson track algorithm detect track larg number dynam move peopl complex scene occlus reli background model requir camera ground plane calibr make use inform past henc impos restrict suitabl onlin applic experi show method yield good track perform larg varieti high dynam scenario typic surveil video webcam footag sport sequenc demonstr algorithm outperform method reli addit inform furthermor analyz influenc differ algorithm compon robust

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