Adaptive Fusion of RGBD Data for twostream FCNbased Level Set Tracking In this paper, we address the contour tracking task based on a deep fully convolutional network (FCN)driven Level Set method. Different from previous Level Setbased trackers wherein speed functions are usually modeled by handcrafted features. In our work by considering the merits of FCN on its powerful feature representation and endtoend pixelwise prediction capabilities, we aim to integrate it into Level Set contour evolution framework. Specifically, we import RGBD data and design a twostream FCNbased structure. An Adaptive fusion mechanism is applied to leverage these two complementary modalities. We adopt the final fusion map from the twostream FCN output as a guidance to evolve the contour more efficiently and accurately. Experimental results on a number of video clips demonstrate the effectiveness and robustness of our proposed method, especially for tackling the challenging problems, i.e., similar appearance between target and background, and low illumination, etc.

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.