NDLI: GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory

Content Provider	IEEE Xplore Digital Library
Author	Xiaolong Zhou Youfu Li Bingwei He Tianxiang Bai
Copyright Year	2005
Abstract	Tracking multiple moving targets in a video is a challenge because of several factors, including noisy video data, varying number of targets, and mutual occlusion problems. The Gaussian mixture probability hypothesis density (GM-PHD) filter, which aims to recursively propagate the intensity associated with the multi-target posterior density, can overcome the difficulty caused by the data association. This paper develops a multi-target visual tracking system that combines the GM-PHD filter with object detection. First, a new birth intensity estimation algorithm based on entropy distribution and coverage rate is proposed to automatically and accurately track the newborn targets in a noisy video. Then, a robust game-theoretical mutual occlusion handling algorithm with an improved spatial color appearance model is proposed to effectively track the targets in mutual occlusion. The spatial color appearance model is improved by incorporating interferences of other targets within the occlusion region. Finally, the experiments conducted on publicly available videos demonstrate the good performance of the proposed visual tracking system.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	1064
Ending Page	1076
Page Count	13
File Size	2883418
File Format	PDF
ISSN	15513203
Volume Number	10
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Noise Pediatrics Entropy Image color analysis Noise measurement Estimation mutual occlusion handling Birth intensity estimation Gaussian mixture probability hypothesis density (GM-PHD) filter multi-target visual tracking (MTVT)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Computer Science Applications

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

Adaptive target birth intensity for Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter

Visual target tracking in occlusion condition: A GM-PHD-based approach

An improvement on GM-PHD filter for occluded target tracking

Probability hypothesis density filter with adaptive estimation of target birth intensity

An improved PHD filter based on variational Bayesian method for multi-target tracking

PHD filter for multi-target visual tracking with trajectory recognition

Novel N-scan GM-PHD-based approach for multi-target tracking

Birth intensity online estimation in GM-PHD filter for multi-target visual tracking

A sequential GM-based PHD filter for a linear Gaussian system

GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory

Similar Documents

Adaptive target birth intensity for Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter

Visual target tracking in occlusion condition: A GM-PHD-based approach

An improvement on GM-PHD filter for occluded target tracking

Probability hypothesis density filter with adaptive estimation of target birth intensity

An improved PHD filter based on variational Bayesian method for multi-target tracking

PHD filter for multi-target visual tracking with trajectory recognition

Novel N-scan GM-PHD-based approach for multi-target tracking

Birth intensity online estimation in GM-PHD filter for multi-target visual tracking

A sequential GM-based PHD filter for a linear Gaussian system

GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory