A framework for heading-guided recognition of human activity
Computer Vision and Image Understanding
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
A hybrid frame concealment algorithm for H.264/AVC
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
Recovery of lost or erroneously received motion vectors
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
Computer Vision and Image Understanding
Kalman filter based error resilience for h.264 motion vector recovery
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
A Robust Finger Tracking Method for Multimodal Wearable Computer Interfacing
IEEE Transactions on Multimedia
Low-complexity video error concealment for mobile applications using OBMA
IEEE Transactions on Consumer Electronics
Enhanced Error Concealment With Mode Selection
IEEE Transactions on Circuits and Systems for Video Technology
Multiple Object Tracking Via Species-Based Particle Swarm Optimization
IEEE Transactions on Circuits and Systems for Video Technology
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Error concealment at the decoder side is an economical approach to ensuring an acceptable and stable video quality in case of packet erasure or loss, and thus it has attracted lots of research interest. Current techniques mainly employ the spatial or temporal correlation to predict the motion vectors (MVs) of the missing blocks, and interpolation, extrapolation or boundary matching schemes are usually effective. However, for heavily corrupted sequences, e.g., with macroblock loss rate beyond 50%, most methods might perform less satisfactorily. Inspired by the tracking efficiency of Kalman filter (KF), in the present work, we adopted it to predict the missing MVs, and the unpredicted ones (minority) were recovered complementarily using the bilinear motion field interpolation (MFI) method. Since the KF prediction is independent of the loss rate, the present framework is especially robust for heavily corrupted videos. Experimental results on typical sequences reveal that the proposed algorithm outperforms the boundary matching algorithm embedded in the H.264/AVC reference code, the MFI algorithm in the literature, and some other existing techniques by up to about 5.68dB.