Title :
Temporal Conditional Random Fields: A conditional state space predictor for visual tracking
Author :
Shafiee, M.J. ; Azimifar, Z. ; Fieguth, P.
Author_Institution :
Comput. Vision & Pattern Recognition Lab., Shiraz Univ., Shiraz, Iran
Abstract :
We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error.
Keywords :
image motion analysis; image sequences; object tracking; conditional state space predictor; object motion; object tracking; optical flow; temporal conditional random fields; visual tracking; Computational modeling; Computer vision; Dynamics; Hidden Markov models; Image motion analysis; Kalman filters; Predictive models; Conditional Random Fields; Feature Function; State Space Predictor; Visual Tracking;
Conference_Titel :
Machine Vision and Image Processing (MVIP), 2010 6th Iranian
Conference_Location :
Isfahan
Print_ISBN :
978-1-4244-9706-5
DOI :
10.1109/IranianMVIP.2010.5941137