NDLI: Robust abandoned object detection and analysis based on online learning

Content Provider	IEEE Xplore Digital Library
Author	Lin Chang Hongmei Zhao Sen Zhai Yafei Ma Hong Liu
Copyright Year	2013
Description	Author affiliation: Shenzhen Nat. Eng. Lab., Digital Telev. Co., Ltd., Shenzhen, China (Lin Chang; Yafei Ma) \|\| Eng. Lab. on Intell. Perception for Internet of Things(ELIP), Peking Univ., Beijing, China (Hongmei Zhao; Sen Zhai; Hong Liu)
Abstract	In this paper, we propose a novel approach based on online learning for accurate and effective detection of abandoned objects. Most existing methods for abandoned objects detection only detect abandoned objects without considering of the logic owner of the abandoned object. These methods need an advanced trained human detector to discriminate abandoned objects from still persons frequently. However, human detection is a challenge in robotic vision system, which always needs off-line training. The proposed framework without specific advanced trained human detector is able to detect abandoned objects and analyze their owners. The online framework is based on a valid assumption for objects and persons in natural scenes. Based on the assumptions that objects are moved by their logic owners and all the moving objects are humans in the scene, online classifiers are established with a certain moving objects just in the scene, which can assist us to detect abandoned objects and analyze their owner in true sense. Instead of a pixel based background model, a robust block based background model is established using online boosting method, which is able to adapt to a large variety of environment and complex changes. In the evaluation over the PETS 2006 and AVSS 2007 datasets, the proposed technique performs robustly and efficiently.
Sponsorship	IEEE Robot. Autom. Soc.
Starting Page	940
Ending Page	945
File Size	4227521
Page Count	6
File Format	PDF
ISBN	9781479927449
DOI	10.1109/ROBIO.2013.6739583
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Boosting Robustness Object detection Training Computational modeling Robots Detectors
Content Type	Text
Resource Type	Article

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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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