2014/04/08 by Duc Phu Chau, François Brémond, Chau, Duc Phu +7
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Analysis and Summarization #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1404.2005
IEEE Winter Conference on Applications of Computer Vision (WACV 2014) (2014)
arxiv created 2014/04/08 · openalex publication_date 2014/04/08 · arxiv updated 2014/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The tracking algorithm performance depends on video content. This paper presents a new multi-object tracking approach which is able to cope with video content variations. First the object detection is improved using Kanade- Lucas-Tomasi (KLT) feature tracking. Second, for each mobile object, an appropriate tracker is selected among a KLT-based tracker and a discriminative appearance-based tracker. This selection is supported by an online tracking evaluation. The approach has been experimented on three public video datasets. The experimental results show a better performance of the proposed approach compared to recent state of the art trackers.