Online multi-object tracking combining optical flow and compressive tracking in Markov decision process

作     者：Yang, Tao Cappelle, Cindy Ruichek, Yassine El Bagdouri, Mohammed 

作者机构：Univ Bourgogne Franche Comte Le2i EA7508 CNRS Arts & MetiersUTBM F-90010 Belfort France 

出 版 物：《JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION》 (可视通信与图像显示杂志)

年 卷 期：2019年第58卷第Jan.期

页      面：178-186页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0835[工学-软件工程] 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：Multi-object tracking Markov decision process Tracking-learning-detection Compressive sensing features 

摘      要：Effective features are important for visual tracking, and efficiency also needs to be considered especially for multi-object tracking. Thanks to the simplicity, we think compressive sensing features are suitable for this task. In this paper, we use compressive sensing features to improve the Markov decision process (MDP) multi-object tracking framework. First, we design a single object tracker which uses the compressive tracking to correct the optical flow tracking and apply this tracker into the MDP tracking framework. The appearance model constructed during compressive tracking also helps for data association. In order to validate our method, we firstly test the designed single object tracker with a common dataset. Then, we test our multi-object tracking method for vehicle tracking. Finally, we analyze and test our approach in the multi-object tracking (MOT) benchmark for pedestrian tracking. The results show our approach performs superiorly against several state-of-the-art online multi-object trackers. (C) 2018 Elsevier Inc. All rights reserved.