Performance Analysis of Machine Learning Classification Algorithms in Static Object Detection for Video Surveillance Applications
Authors:
- 1 November 2020
- journal article
- research article
- Published by Springer Nature in Wireless Personal Communications
- Vol. 115 (2) , 1291-1307
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Unsupervised detection and tracking of moving objects for video surveillance applicationsPattern Recognition Letters, 2016
- Statistical feature bag based background subtraction for local change detectionInformation Sciences, 2016
- A Study on Classification for Static and Moving Object in Video Surveillance SystemInternational Journal of Image, Graphics and Signal Processing, 2016
- Moving object detection using unstable camera for video surveillance systemsOptik, 2015
- Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban TrafficJournal of Signal Processing Systems, 2015
- Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual SurveillanceIEEE Transactions on Information Forensics and Security, 2015
- Patch-based local histograms and contour estimation for static foreground classificationEURASIP Journal on Image and Video Processing, 2015
- Video Segmentation Framework Based on Multi-kernel Representations and Feature Relevance Analysis for Object Classification2014
- Extraction of stable foreground image regions for unattended luggage detectionMultimedia Tools and Applications, 2014
- Detection of static groups and crowds gathered in open spaces by texture classificationPattern Recognition Letters, 2014