On the Detection of Motion and the Computation of Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time recognition of activity using temporal templates
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Gesture Recognition Using Temporal Template Based Trajectories
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
A novel four-step search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Hybrid fire detection using hidden Markov model and luminance map
Computers and Electrical Engineering
Sports video summarization based on motion analysis
Computers and Electrical Engineering
A vision-based blind spot warning system for daytime and nighttime driver assistance
Computers and Electrical Engineering
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A motion field generation algorithm using block matching of edge flag histograms has been developed aiming at its application to motion recognition systems. Use of edge flags instead of pixel intensities has made the algorithm robust against illumination changes. In order to detect local motions of interest effectively, a new adaptive frame interval adjustment scheme has been introduced in which only the edge flags due to local motions present in the frame are accumulated and utilized in block matching. These edge flags are projected onto x and y axes to generate histograms and the motion in x and y directions are determined by histogram matching. As a result, the computational cost for best match search has been substantially reduced. A vector representation of the motion field, which is called projected principal-motion distribution (PPMD), has also been proposed. It was applied to preliminary motion recognition experiments using Hidden Markov Models (HMMs) and its effectiveness has been confirmed. Moreover the advantage of the proposed motion field generation method over the simple optical flow as well as the conventional block matching method using pixel intensities has been demonstrated.