NDLI: Individual and group tracking with the evaluation of social interactions

Content Provider	IET Digital Library
Author	Yigit, Ahmet Temizel, Alptekin
Abstract	Tracking groups of people is a challenging problem. Groups may grow or shrink dynamically with merging and splitting of individuals and conventional trackers are not designed to handle such cases. In this study, the authors present a conjoint individual and group tracking (CIGT) framework based on particle filter and online learning. CIGT has four complementary phases: two-phase association, false positive elimination, tracking and learning. First, reliable tracklets are created and detection responses are associated to tracklets in two-phase association. Then, hierarchal false positive elimination is performed for unassociated detection responses. In the tracking phase, CIGT calculates multiple weights from the observation and jointly models individuals and groups. Particle advection is used in the motion model of CIGT to facilitate tracking of dense groups. In the learning phase, the discriminative appearance model, consisting of shape, colour and texture features, is extracted and used in AdaBoost online learning. Using the discriminative learning model, state estimation is performed on both individuals and groups. The experimental results show that the performance of the proposed framework compares favourably with other individual and group-tracking methods for both real and synthetic datasets.
Starting Page	255
Ending Page	263
Page Count	9
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 3, Apr (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0238
Journal	IET Computer Vision
Publisher Date	2016-12-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AdaBoost Online Learning CIGT Framework CIGT Motion Model Colour Feature Extraction Computer Vision And Image Processing Technique Conjoint Individual And Group Tracking Framework Detection Responses Discriminative Appearance Model Discriminative Learning Model False Positive Elimination Feature Extraction Filtering Method in Signal Processing Hierarchal False Positive Elimination Image Colour Analysis Image Filtering Image Motion Analysis Image Recognition Image Texture Knowledge Engineering Technique Learning in AI Object Detection Object Tracking Online Learning Phase Particle Advection Particle Filter Reliable Tracklets Shape Feature Extraction Social Interaction Evaluation State Estimation Texture Feature Extraction Two-phase Association Unassociated Detection Response
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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
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