Nonrigid object trackerbased on a robust combination of parametric active contour and point distribution model Our study considers the development of a reliable tracker for nonrigid objects evolving on cluttered background in crowded scenes captured by moving cameras. For this purpose, we propose an original method that combines two approaches, respectively based on parametric active contours (PAC) and on point distribution model (PDM). The PAC tracker relies on an effective and efficient implementation of contour convergence mechanism to bring a smooth contour to the edges of the target in realtime. The PDM approach collects feature points in the region delineated by the PAC tracker to build and update a model of the target in term of a feature point distribution. Formally, when a novel frame is considered, its feature points are matched with the PDM model. The matching information is used to initialize the novel PAC, whose convergence identify the points that are relevant to update the PDM for the next frame. Hence, the two approaches complement each others. The a priori information provided by the PDM makes the system robust towards occlusions, while the deformation of the PAC increases its robustness towards target appearance changes. Simulations on realworld video sequences demonstrate the performance of our approach.

A Refined Particle Filter Method for Contour Tracking Traditional particle filter which uses simple geometric shapes for representation cannot track objects with complex shape accurately. In this paper, we propose a refined particle filter method for contour tracking based on a binary level set model. In contrast with other previous work, the computational efficiency is greatly improved due to the simple form of the level set function. In addition, we perform curve evolution in the update step to make good use of the observation at current time. Finally, we consider some appearance information as well as the energy function to measure the weight for particles, which can identify the target more accurately. Experiment results on several challenging video sequences have verified the proposed algorithm is efficient and effective in many complicated scenes.