3D object tracking via boundary constrained region-based model

Song Zhao, LingFeng Wang, Wei Sui, Huai-Yu Wu, Chunhong Pan

Abstract: In this paper, we propose a method for joint 2D segmentation and 2D-3D pose tracking. First, we define a novel energy functional which considers the discrimination between statistical appearance models and the coherence among neighboring pixels simultaneously. And then, a particle filter-like stochastic optimization technique is adopted to solve the energy functional, so that a preferable initial value can be provided for the subsequent damped Newton optimization method. Furthermore, an occlusion-aware updating strategy is utilized for appearance models, which can easily increase the foreground learning rate. As a result, our method is more suitable for the video sequences with occlusion. Experimental results highlight excellent performance on challenging synthetic and real-world sequences as compared with the state-of-the-art approaches.