research-article

Object recognition and tracking for remote video surveillance

Author:

G. L. ForestiAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 9, Issue 7

Pages 1045 - 1062

https://doi.org/10.1109/76.795058

Published: 01 October 1999 Publication History

Abstract

A system for real-time object recognition and tracking for remote video surveillance is presented. In order to meet real-time requirements, a unique feature, i.e., the statistical morphological skeleton, which achieves low computational complexity, accuracy of localization, and noise robustness has been considered for both object recognition and tracking. Recognition is obtained by comparing an analytical approximation of the skeleton function extracted from the analyzed image with that obtained from model objects stored into a database. Tracking is performed by applying an extended Kalman filter to a set of observable quantities derived from the detected skeleton and other geometric characteristics of the moving object. Several experiments are shown to illustrate the validity of the proposed method and to demonstrate its usefulness in video-based applications

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Chen GZhu PCao BWang XHu Q(2023)Cross-Drone Transformer Network for Robust Single Object TrackingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.328155733:9(4552-4563)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3281557
Hu YXu Y(2021)Study on the Automatic Basketball Shooting System Based on the Background Subtraction MethodScientific Programming10.1155/2021/26346622021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2634662
Mehrish ASingh PJain PSubramanyam AKankanhalli M(2020)Egocentric Analysis of Dash-Cam Videos for Vehicle ForensicsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.292956130:9(3000-3014)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1109/TCSVT.2019.2929561
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Object recognition and tracking for remote video surveillance
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning
    1. Machine learning algorithms

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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 9, Issue 7

October 1999

161 pages

ISSN:1051-8215

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IEEE Press

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Published: 01 October 1999

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Chen GZhu PCao BWang XHu Q(2023)Cross-Drone Transformer Network for Robust Single Object TrackingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.328155733:9(4552-4563)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3281557
Hu YXu Y(2021)Study on the Automatic Basketball Shooting System Based on the Background Subtraction MethodScientific Programming10.1155/2021/26346622021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2634662
Mehrish ASingh PJain PSubramanyam AKankanhalli M(2020)Egocentric Analysis of Dash-Cam Videos for Vehicle ForensicsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.292956130:9(3000-3014)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1109/TCSVT.2019.2929561
Garcia-Garcia AOrts-Escolano SGarcia-Rodriguez JCazorla M(2018)Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensorsJournal of Real-Time Image Processing10.5555/3204235.320428514:3(585-604)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.5555/3204235.3204285
Ratnarajah AGoonetilleke STissera DBalagopalan KRodrigo R(2018)Moving Object Based Collision-Free Video Synopsis2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00287(1658-1663)Online publication date: 7-Oct-2018
https://dl.acm.org/doi/10.1109/SMC.2018.00287
Elafi IJedra MZahid N(2016)Unsupervised detection and tracking of moving objects for video surveillance applicationsPattern Recognition Letters10.1016/j.patrec.2016.08.00884:C(70-77)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.patrec.2016.08.008
Bhaumik HBhattacharyya SNath MChakraborty S(2016)Hybrid soft computing approaches to content based video retrievalApplied Soft Computing10.1016/j.asoc.2016.03.02246:C(1008-1029)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.03.022
Tang ALu KWang YHuang JLi H(2015)A Real-Time Hand Posture Recognition System Using Deep Neural NetworksACM Transactions on Intelligent Systems and Technology10.1145/27359526:2(1-23)Online publication date: 31-Mar-2015
https://dl.acm.org/doi/10.1145/2735952
Isaza CSalas JRaducanu B(2014)Rendering ground truth data sets to detect shadows cast by static objects in outdoorsMultimedia Tools and Applications10.1007/s11042-013-1409-970:1(557-571)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1007/s11042-013-1409-9
Isaza CSalas JRaducanu B(2012)Synthetic ground truth dataset to detect shadows cast by static objects in outdoorsProceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications10.1145/2304496.2304507(1-6)Online publication date: 21-May-2012
https://dl.acm.org/doi/10.1145/2304496.2304507
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Object tracking and local appearance capturing in a remote scene video surveillance system with two cameras

An intelligent video security system using object tracking and shape recognition

Tracking with Multiple Cameras for Video Surveillance

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