NDLI: Vehicle tracking based on co-learning particle filter

Content Provider	IEEE Xplore Digital Library
Author	Weilong Ye Huaping Liu Fuchun Sun Meng Gao
Copyright Year	2009
Description	Author affiliation: Shijiazhuang Railway Institute, Hebei, China (Meng Gao) \|\| Department of Computer Science and Technology, Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, TNLIST, Beijing, China (Weilong Ye; Huaping Liu; Fuchun Sun)
Abstract	In this paper, we propose a co-learning particle filter approach for vehicle tracking, which is very important for intelligent vehicle. The proposal distribution of the particle filter is a combination of an extra support vector machine (SVM) detector and the motion prior. Previous works focusing on how to online update the detector or the observation likelihood using the tracking results. These approaches belong to “self-learning” fashion and easily tend to drift. The major difference between the proposed approach and previous works is that the SVM detector and the likelihood function can be mutually updated in a co-learning manner. By adopting the co-learning technology, the unlabelled samples which are generated during tracking are utilized to progressively modify the SVM detector and update the observation likelihood; therefore the resulting tracker is more robust and effectively avoids the drift problem. Finally, the performance of the proposed approach is evaluated using extensive real visual tracking examples.
Starting Page	2979
Ending Page	2984
File Size	519925
Page Count	6
File Format	PDF
ISBN	9781424438037
DOI	10.1109/IROS.2009.5354574
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle tracking Particle filters Radar tracking Detectors Semisupervised learning Intelligent vehicles Support vector machines Intelligent robots Sun Proposals
Content Type	Text
Resource Type	Article

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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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

Incremental Co-Boost for visual tracking

Vehicle tracking using stochastic fusion-based particle filter

Semi-supervised ensemble tracking

Semi-supervised particle filter for visual tracking

On-road computer vision based obstacle detection

Symmetry-Aided Particle Filter for Vehicle Tracking

Multi-pedestrian tracking based on feature learning method with lateral inhibition

Unmanned surface vehicle target tracking based on marine radar

Particle filter based multi-pedestrian tracking by HOG and HOF

Vehicle tracking based on co-learning particle filter

Similar Documents

Incremental Co-Boost for visual tracking

Vehicle tracking using stochastic fusion-based particle filter

Semi-supervised ensemble tracking

Semi-supervised particle filter for visual tracking

On-road computer vision based obstacle detection

Symmetry-Aided Particle Filter for Vehicle Tracking

Multi-pedestrian tracking based on feature learning method with lateral inhibition

Unmanned surface vehicle target tracking based on marine radar

Particle filter based multi-pedestrian tracking by HOG and HOF

Vehicle tracking based on co-learning particle filter