NDLI: Learning People Detectors for Tracking in Crowded Scenes

Content Provider	IEEE Xplore Digital Library
Author	Siyu Tang Andriluka, M. Milan, A. Schindler, K. Roth, S. Schiele, B.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Tech. Univ. Darmstadt, Darmstadt, Germany (Milan, A.; Roth, S.) \|\| Max Planck Inst. for Inf., Saarbrucken, Germany (Siyu Tang; Andriluka, M.; Schiele, B.) \|\| Photogrammetry & Remote Sensing Group, ETH Zurich, Zurich, Switzerland (Schindler, K.)
Abstract	People tracking in crowded real-world scenes is challenging due to frequent and long-term occlusions. Recent tracking methods obtain the image evidence from object (people) detectors, but typically use off-the-shelf detectors and treat them as black box components. In this paper we argue that for best performance one should explicitly train people detectors on failure cases of the overall tracker instead. To that end, we first propose a novel joint people detector that combines a state-of-the-art single person detector with a detector for pairs of people, which explicitly exploits common patterns of person-person occlusions across multiple viewpoints that are a frequent failure case for tracking in crowded scenes. To explicitly address remaining failure modes of the tracker we explore two methods. First, we analyze typical failures of trackers and train a detector explicitly on these cases. And second, we train the detector with the people tracker in the loop, focusing on the most common tracker failures. We show that our joint multi-person detector significantly improves both detection accuracy as well as tracker performance, improving the state-of-the-art on standard benchmarks.
Sponsorship	IEEE Comput. Soc.
Starting Page	1049
Ending Page	1056
File Size	1247226
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.134
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Joints Training Legged locomotion Target tracking Trajectory Tracking loops Synthetic Training Data Pedestrian Detection Tracking Multiple People Tracking Occlusion Handling Structured SVM
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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