Combining the Advantages of Local and Global Optic Flow Methods
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Good Features to Track
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Unsupervised Bayesian Detection of Independent Motion in Crowds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Object tracking in the presence of occlusions via a camera network
Proceedings of the 6th international conference on Information processing in sensor networks
Crowd Behavior Recognition for Video Surveillance
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
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
Variational optical flow computation in real time
IEEE Transactions on Image Processing
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In this paper we present an approach to speed up the computation of sparse optical flow fields by means of integral images and provide implementation details. Proposing a modification of the Lucas-Kanade energy functional allows us to use integral images and thus to speed up the method notably while affecting only slightly the quality of the computed optical flow. The approach is combined with an efficient scanline algorithm to reduce the computation of integral images to those areas where there are features to be tracked. The proposed method can speed up current surveillance algorithms used for scene description and crowd analysis.