Robust Multiple Car Tracking with Occlusion Reasoning
Robust Multiple Car Tracking with Occlusion Reasoning
Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Robust Salient Motion Detection with Complex Background for Real-Time Video Surveillance
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Real Time Foreground-Background Segmentation Using a Modified Codebook Model
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Prediction-oriented dimensionality reduction of industrial data sets
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
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Moving objects detection is a crucial step for video surveillance systems. The segmentation performed by motion detection algorithms is often noisy, which makes it hard to distinguish between relevant motion and noise motion. This article describes a new approach to make such a distinction using principal component analysis (PCA), a technique not commonly used in this domain. We consider a ten-frame subsequence, where each frame is associated with one dimension of the feature space, and we apply PCA to map data in a lower-dimensional space where points picturing coherent motion are close to each other. Frames are then split into blocks that we project in this new space. Inertia ellipsoids of the projected blocks allow us to qualify the motion occurring within the blocks. The results obtained are encouraging since we get very few false positives and a satisfying number of connected components in comparison to other tested algorithms.