A buyer's guide to conic fitting
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
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
A Real-Time System for Monitoring of Cyclists and Pedestrians
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
A revaluation of frame difference in fast and robust motion detection
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
The Undecimated Wavelet Decomposition and its Reconstruction
IEEE Transactions on Image Processing
Optimal wavelet differencing method for robust motion detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Determination of road traffic parameters based on 3d wavelet representation of an image sequence
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Moving object detection in video sequences presents an important problem in computer vision. If a video sequence is generated by a stationary video camera, one usually attempts to build a statistical model of the background and an appropriate statistical test to classify pixels into foreground or background. This approach is efficient for many laboratory test sequences, but may render itself inadequate in real-life surveillance systems with many additional scene-, illumination- and camera-related phenomena. In this case better results can be obtained with frame differencing scheme, that is unfortunately prone to aperture problem and leads to inconsistent detections. In this paper, we propose a multiresolution frame differencing technique. Each frame is first decomposed into undecimated wavelet transform coefficients and after that, differencing scheme is applied on wavelet coefficients in several bands separately. These band-dependent motion detections alleviate the aperture problem and when fused, they produce more consistent moving object detection. The obtained detection results greatly facilitate later processing steps, like object tracking and recognition.