A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient region-based motion segmentation for a video monitoring system
Pattern Recognition Letters
An edge-based approach to motion detection
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
MTES: visual programming environment for teaching and research in image processing
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
A real-time system for video surveillance of unattended outdoor environments
IEEE Transactions on Circuits and Systems for Video Technology
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
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Background modeling is one of the most challenging and time consuming tasks in moving object detection for video surveillance. In this paper, we present a new algorithm which does not require any background model. Instead, it utilizes three most recent consecutive frames to detect the presence of moving object by extracting moving edges. In the proposed method, we introduce an edge segment based approach instead of traditional edge pixel based approach. We also utilize an efficient edge-matching algorithm which reduces the variation of edge localization in different frames. Finally, regions of the moving objects are extracted from previously detected moving edges by using an efficient watershed based segmentation algorithm. The proposed method is characterized through robustness against the random noise, illumination variations and quantization error and is validated with the extensive experimental results.