Recent trends in multicue based visual tracking: A review

• Published in: Expert systems with applications 2020-12, Vol.162, p.113711, Article 113711
• Main Authors: Kumar, Ashish, Walia, Gurjit Singh, Sharma, Kapil
• Format: Article
• Language: English
• Subjects: Benchmarks; Computer vision; Deep learning; Feature extraction; Multicue; Optical tracking; Tracking evaluation; Trends; Visual tracking
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113711

•A comprehensive survey of single/multimodal multicue object tracking.•Review of traditional multicue methods and categorized into four different category.•Review of recent deep learning based multicue methods from two different perspective.•Summarize various single/multimodal benchmark for multicue object tracking.•Experimental evaluation of state-of-the-arts on various benchmark datasets. In the recent years, multicue visual tracking frameworks have been preferred over single cue visual tracking approaches to address critical environmental challenges. In literature, it has been well accepted that combining multiple complementary cues extracted from single sensor or multiple sensors, deep features and features extracted from different layers of deep learning architecture enhance tracking performance and accuracy. In this paper, we have categorized the multi-cue object tracking work based on the exploited appearance model into traditional architecture and deep learning based trackers. The categorized work have been tabulated to provide detailed overview of the representative work and to list out the new trends in the domain. Also, we have briefly analyzed the various tracking benchmark and tabulated their substantial parameters. Our review work analyze the recent trends in the field of object tracking alongwith the latest tracking benchmark to indicate the future directions to the researchers. In addition, we have experimentally evaluated the state-of-the-arts on OTB-15, UAV123, VOT2017 and LaSOT datasets under various tracking challenges.