Supporting timeliness and accuracy in distributed real-time content-based video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Real-time foveation techniques for low bit rate video coding
Real-Time Imaging
Real-time video content analysis: QoS-aware application composition and parallel processing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Foveation embedded DCT domain video transcoding
Journal of Visual Communication and Image Representation
Synoptic maps forecast using spatio-temporal models
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Smoothing of optical flow using robustified diffusion kernels
Image and Vision Computing
Robust processing of optical flow of fluids
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropriately classifying the unlabeled and the occluding regions. Segmentation and optical flow tracking is used for predicting future frames