We are happy to announce that the following paper has been accepted to the 10th International Conference on Computer Vision Theory and Applications (VISAPP) that will take place in Berlin, on March 11 - 14, 2015: A self-adaptive likelihood function for tracking with particle filter Séverine Dubuisson (1), Myriam Robert-Seidowsky (2) and Jonathan Fabrizio (2) (1) CNRS, UMR 7222, ISIR, F-75005, Paris, France (2) LRDE-EPITA, 14-16, rue Voltaire, F-94276, Le Kremlin Bicêtre, France Abstract: The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts ``on-line" two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance. On one hand, our experimental results show that the common settings of particle filter are sub-optimal. On another hand, we prove that our approach achieves a lower tracking error without needing tuning these parameters. Our adaptive method allows to track objects in complex conditions (illumination changes, cluttered background, etc.) without adding any computational cost compared to the common usage with fixed parameters. -- Myriam Robert-Seidowsky - LRDE/EPITA