Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines

作     者：Liu, Chenghuan Huynh, Du Q. Reynolds, Mark 

作者机构：Univ Western Australia Sch Comp Sci & Software Engn Perth WA 6009 Australia 

出 版 物：《IEEE TRANSACTIONS ON CYBERNETICS》 (IEEE Trans. Cybern.)

年 卷 期：2020年第50卷第4期

页      面：1726-1738页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：China Scholarship Council 

主　　题：Target tracking Correlation Visualization Partitioning algorithms Probabilistic logic Adaptation models Support vector machines Correlation filters occlusion handling probabilistic finite state machines visual tracking 

摘      要：Visual tracking has been an active research area in computer vision for decades. However, the performance of existing techniques is still challenged by various factors, such as occlusion and change in appearance of the target. In this paper, we propose a novel framework based on correlation filtering and probabilistic finite state machines (FSMs) to handle occlusion. In our tracking framework, the target is partitioned into several parts whose occlusion states are automatically detected. A set of states for the target is defined in terms of the combination of the parts occlusion states. The probabilistic FSMs are then used to model the target s state transitions so as to reduce the effect of noise in the output response maps of correlation filters. Our target model s update strategy is adaptable online depending on the estimated state of the target. Extensive experiments have been performed on several public benchmarks and the proposed algorithm achieves competitive results against state-of-the-art techniques.