NDLI: An integrated Monte Carlo data association framework for multi-object tracking

Content Provider	IEEE Xplore Digital Library
Author	Jianru Xue Nanning Zheng Xiaopin Zhong
Copyright Year	2006
Description	Author affiliation: Inst. of Artificial Intelligence & Robotics, Xi'an Jiaotong Univ. (Jianru Xue; Nanning Zheng; Xiaopin Zhong)
Abstract	We propose a sequential Monte Carlo data association algorithm based on a two-level computational framework for tracking varying number of interacting objects in dynamic scene. Firstly we propose a hybrid measurements generation process to facilitate varying number problems, the process mixes target-oriented measurements provided by target dynamics prior model and data-oriented measurements based on discriminative model. Secondly an improved Monte Carlo joint data association filter is used to combat the curse of dimension problem. Finally the particle based belief propagation is used to facilitate interactions among objects. This framework integrates discriminative model learning, Monte Carlo joint data association filtering, and belief propagation algorithm, these methods are realized as different levels of approximation to an 'ideal' generative model of multiple visual targets tracking, and result in a novel sequential Monte Carlo data association algorithm. The algorithm is illustrated via tracking many pedestrians in a real video sequence
Sponsorship	IEEE CPS
Starting Page	703
Ending Page	706
File Size	196747
Page Count	4
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.254
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Target tracking Filtering Filters Sliding mode control Time measurement Yttrium Layout Belief propagation Approximation algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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

Discriminatively trained particle filters for complex multi-object tracking

Extended object tracking with unknown association, missing observations, and clutter using particle filters

Performance comparison of several nonlinear multi-Bernoulli filters for multi-target filtering

Particle filters and data association for multi-target tracking

Sequential monte carlo implementation of the phd filter for multi-target tracking

Random finite sets and sequential Monte Carlo methods in multi-target tracking

Tracking variable number of targets using sequential monte carlo methods

Sequential Monte Carlo filtering for multi-aspect detection/tracking

Incremental density approximation and kernel-based Bayesian filtering for object tracking

An integrated Monte Carlo data association framework for multi-object tracking

Similar Documents

Discriminatively trained particle filters for complex multi-object tracking

Extended object tracking with unknown association, missing observations, and clutter using particle filters

Performance comparison of several nonlinear multi-Bernoulli filters for multi-target filtering

Particle filters and data association for multi-target tracking

Sequential monte carlo implementation of the phd filter for multi-target tracking

Random finite sets and sequential Monte Carlo methods in multi-target tracking

Tracking variable number of targets using sequential monte carlo methods

Sequential Monte Carlo filtering for multi-aspect detection/tracking

Incremental density approximation and kernel-based Bayesian filtering for object tracking

An integrated Monte Carlo data association framework for multi-object tracking