Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boxlets: a fast convolution algorithm for signal processing and neural networks
Proceedings of the 1998 conference on Advances in neural information processing systems II
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Image and Video Processing
Handbook of Image and Video Processing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
A Sparse Probabilistic Learning Algorithm for Real-Time Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On pre-detect template matching
International Journal of Computer Applications in Technology
Automatic measuring system for railroad wheels
International Journal of Computer Applications in Technology
Adaptive method for improvement of human skin detection in colour images
International Journal of Computer Applications in Technology
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Object detection and tracking is one of the most popular areas of video processing and the essential requirement of any surveillance system. A fast approach for person detection and tracking is presented. This work proposes to do target tracking with Kalman filter. In case of a misdetection, which would lead a wrong update of the filter, a fast mean shift iteration based on integral computation is performed to propose a more accurate detection. Tracking results are demonstrated for complex scenes and evaluation of the proposed tracking approach is presented.