research-article

Unusual activity detection for video surveillance

Authors:

Sarvesh Vishwakarma,

Anupam Agrawal,

M. D. TiwariAuthors Info & Claims

IITM '10: Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

Pages 297 - 305

https://doi.org/10.1145/1963564.1963616

Published: 27 December 2010 Publication History

Abstract

ideo surveillance has gained importance in law enforcement, security and military applications. The system consists of processing steps such as object detection, movement tracking, and activity monitoring. The paper contribution is to present the human activity analysis system that both detect a human with carrying or abandoning an object and segments the object from the human so that it can be tracked. Segmentation of objects is done from the background using advance Gaussian mixture model. The tracking algorithm considers the human as whole from frame to frame, it does not track the human parts such as limbs. Object features such as center of mass, size, and bounding box are used in this paper to estimate a matching between objects in consecutive frames. As the object is segmented and tracked, Bayesian inference framework is used for event analysis. This system uses a single camera view and unusual activity is detected using the detected objects and object tracking result. The operator is notified if an unusual activity is detected.

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Cited By

Ahamad RMishra K(2023)Knowledge discovery of suspicious objects using hybrid approach with video clips and UAV images in distributed environments: a novel approachWireless Networks10.1007/s11276-023-03394-629:8(3393-3416)Online publication date: 13-Jun-2023
https://doi.org/10.1007/s11276-023-03394-6
Ahamad RMishra K(2023)Hybrid approach for suspicious object surveillance using video clips and UAV images in cloud-IoT-based computing environmentCluster Computing10.1007/s10586-023-03977-027:1(761-785)Online publication date: 17-Feb-2023
https://doi.org/10.1007/s10586-023-03977-0
Vishwakarma SChilwal B(2023)Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice CropMathematics and Computer Science Volume 210.1002/9781119896715.ch4(49-58)Online publication date: 11-Jul-2023
https://doi.org/10.1002/9781119896715.ch4
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Index Terms

Unusual activity detection for video surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

cover image ACM Other conferences

IITM '10: Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia

December 2010

355 pages

ISBN:9781450304085

DOI:10.1145/1963564

Editors:
M. D. Tiwari
IIIT Allahabad, India
,
R. C. Tripathi
IIIT Allahabad, India
,
Anupam Agrawal
IIIT Allahabad, India

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2010

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IITM '10

IITM '10: First International Conference on Intelligent Interactive Technologies and Multimedia

December 27 - 30, 2010

Allahabad, India

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Cited By

Ahamad RMishra K(2023)Knowledge discovery of suspicious objects using hybrid approach with video clips and UAV images in distributed environments: a novel approachWireless Networks10.1007/s11276-023-03394-629:8(3393-3416)Online publication date: 13-Jun-2023
https://doi.org/10.1007/s11276-023-03394-6
Ahamad RMishra K(2023)Hybrid approach for suspicious object surveillance using video clips and UAV images in cloud-IoT-based computing environmentCluster Computing10.1007/s10586-023-03977-027:1(761-785)Online publication date: 17-Feb-2023
https://doi.org/10.1007/s10586-023-03977-0
Vishwakarma SChilwal B(2023)Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice CropMathematics and Computer Science Volume 210.1002/9781119896715.ch4(49-58)Online publication date: 11-Jul-2023
https://doi.org/10.1002/9781119896715.ch4
Tamilmani GUma SSivakumari S(2022)Unusual Event Detection from SurveillanceComputational Methods and Data Engineering10.1007/978-981-19-3015-7_35(477-487)Online publication date: 9-Sep-2022
https://doi.org/10.1007/978-981-19-3015-7_35
Tripathi RJalal AAgrawal S(2019)Abandoned or removed object detection from visual surveillanceMultimedia Tools and Applications10.1007/s11042-018-6472-978:6(7585-7620)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6472-9
Ahmed TPathan AAhmed S(2018)Learning Algorithms for Anomaly Detection from ImagesInternational Journal of System Dynamics Applications10.4018/IJSDA.20150701034:3(43-69)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.4018/IJSDA.2015070103
Tripathi RJalal AAgrawal S(2017)Suspicious human activity recognition: a reviewArtificial Intelligence Review10.1007/s10462-017-9545-750:2(283-339)Online publication date: 28-Feb-2017
https://doi.org/10.1007/s10462-017-9545-7
Crooks ACroitoru ALu XWise SIrvine JStefanidis A(2015)Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity AnalysisISPRS International Journal of Geo-Information10.3390/ijgi40316274:3(1627-1656)Online publication date: 2-Sep-2015
https://doi.org/10.3390/ijgi4031627
Singh AAgrawal A(2013)An interactive framework for abandoned and removed object detection in video2013 Annual IEEE India Conference (INDICON)10.1109/INDCON.2013.6725905(1-6)Online publication date: Dec-2013
https://doi.org/10.1109/INDCON.2013.6725905

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