NDLI: Active tracking and pursuit under different levels of occlusion: a two-layer approach

Content Provider	Springer Nature Link
Author	Baum, Tomer Izhaki, Idan Rivlin, Ehud Katzir, Gadi
Copyright Year	2013
Abstract	We present an algorithm for real-time, robust, vision-based active tracking and pursuit. The algorithm was designed to overcome problems arising from active vision-based pursuit, such as target occlusion. Our method employs two layers to deal with occlusions of different lengths. The first layer is for short- or medium-term occlusions: those where a known method—such as mean shift combined with a Kalman filter—fails. For this layer we designed the hybrid filter for active pursuit (HAP). HAP utilizes a Kalman filter modified to respond to two different modes of action: one in which the target is positively identified and one in which the target identification is uncertain. For long-term occlusions we use the second layer. This layer is a decision algorithm that follows a learning procedure and is based on game theory-related reinforcement (Cesa-Bianchi and Lugosi, Prediction Learning and Games, 2006). The learning process is based on trial and error and is designed to perform adequately with a small number of samples. The algorithm produces a data structure that can be shared among agents or sent to a central control of a multi-agent system. The learning process is designed so that agents perform tasks according to their skills: an efficient agent will pursue targets while an inefficient agent will search for entering targets. These capacities make this system well suited for embedding in a multi-agent control system.
Starting Page	173
Ending Page	184
Page Count	12
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	25
Issue Number	1
e-ISSN	14321769
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-06-12
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bio-inspired computer vision Hybrid system Active pursuit Reinforcement learning Tracking with occlusion Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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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
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