Park, Sung-Kee Kim, Chansu 2024-01-19T11:39:14Z 2024-01-19T11:39:14Z 2022-03-01 2015-08 https://pubs.kist.re.kr/handle/201004/115026 Particle filter is a widely used framework for object tracking, but it is vulnerable when its observation model is based on visual appearance. In this paper, we propose a modified particle filtering that makes use of foreground regions and their pixel-based confidences that are likely to be foreground; the foreground regions are used for preventing generations of particle in the background and the pixel-based confidences are enable to enhance the similarity between foreground and observation models. We evaluate the performance on five datasets and show that the proposed approach outperforms a number of state-of-the-art object tracking methods. English IEEE Modified Particle Filtering using Foreground Separation and Confidence for Object Tracking Conference 1 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) US IOSB, Karlsruhe Inst Technol & Fraunhofer, Karlsruhe, GERMANY 2015-08-25 2015 12TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE (AVSS) 000380619700050 2-s2.0-84958679598