Performance of optical flow techniques
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Real-time and accurate segmentation of moving objects in dynamic scene
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Vehicle classification based on soft computing algorithms
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Visual object tracking system employing fixed and PTZ cameras
Intelligent Decision Technologies
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The solution presented in this paper combines background modelling, shadow detection and morphological and temporal processing into a single system responsible for detection and segmentation of moving objects recorded with a static camera. Vehicles and trains are detected based on their pixel-level difference with respect to a continually updated background model, using a Gaussian mixture calculated separately for every pixel. The shadow detection method utilizes a colour model which allows for estimating chromatic and brightness differences between the pixel colour and the background model. Morphological and temporal operations performed on binary images denoting moving objects include connecting the components, closing and temporal filtering. Experiments carried out involve employing implemented algorithms to detect vehicles and trains in video sequences. The results achieved are described and illustrated in figures.