NDLI: Online Social Behavior Modeling for Multi-target Tracking

Content Provider	IEEE Xplore Digital Library
Author	Shu Zhang Das, A. Chong Ding Roy-Chowdhury, A.K.
Copyright Year	2013
Description	Author affiliation: Univ. of California, Riverside, Riverside, CA, USA (Shu Zhang; Das, A.; Chong Ding; Roy-Chowdhury, A.K.)
Abstract	People are often seen together. We use this simple observation to provide crucial additional information and increase the robustness of a video tracker. The goal of this paper is to show how, in situations where offline training data is not available, a social behavior model (SBM) can be inferred online and then integrated within the tracking algorithm. We start with tracklets (short term confident tracks) obtained using an existing tracker. The SBM, a graphical model, captures the spatio-temporal relationships between the tracklets and is learned online from the video. The final probability of association between the tracklets is obtained by a combination of individual target characteristics (e.g., their appearance), as well as the learned relationship model between them. The entire system is causal whereby the results at any given time depend only upon the part of the video already observed. Experimental results on three state-of-the-art datasets show that, without having access to any offline training data or the entire test video a priori (conditions that may be restrictive for many application domains), our proposed method obtains results similar to those that do impose the above conditions.
Starting Page	751
Ending Page	758
File Size	1046193
Page Count	8
File Format	PDF
ISBN	9780769549903
ISSN	21607516
DOI	10.1109/CVPRW.2013.113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Legged locomotion social behavior model Target tracking Computational modeling multi-target tracking Mathematical model Videos
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

Human feet tracking guided by locomotion model

Model-driven multi-target tracking in crowd scenes

Online anomaly detection in videos by clustering dynamic exemplars

Monocular Human Motion Tracking with Non-Connected Body Part Dependency

Labeled multi-Bernoulli track-before-detect for multi-target tracking in video

Improved mean shift for multi-target tracking

Online multi-person tracking via robust collaborative model

Joint multi-Bernoulli RFS for two-target scenario

Multi-robot Multiple Hypothesis Tracking for pedestrian tracking with detection uncertainty

Online Social Behavior Modeling for Multi-target Tracking

Similar Documents

Human feet tracking guided by locomotion model

Model-driven multi-target tracking in crowd scenes

Online anomaly detection in videos by clustering dynamic exemplars

Monocular Human Motion Tracking with Non-Connected Body Part Dependency

Labeled multi-Bernoulli track-before-detect for multi-target tracking in video

Improved mean shift for multi-target tracking

Online multi-person tracking via robust collaborative model

Joint multi-Bernoulli RFS for two-target scenario

Multi-robot Multiple Hypothesis Tracking for pedestrian tracking with detection uncertainty

Online Social Behavior Modeling for Multi-target Tracking