Uncertain motion tracking based on convolutional net with semantics estimation and region proposals. (June 2020)

• Record Type: Journal Article
• Authors: Zhang, Huanlong; Chen, Jian; Nie, Guohao; Hu, Shiqiang
• Is Part Of: Pattern recognition. Volume 102(2020:Jun.)
• Publication Date: 2020-06
• Subjects: Correlation filter -- Semantics estimation -- Visual tracking -- Region proposals -- Contextual information; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107232
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel uncertain motion tracking framework with semantics estimation and region proposals is proposed. A semantics object proposals generation strategy is proposed. The proposed hybrid semantics tracking algorithm combines the full advantages of globally sparse semantics region proposals prediction and correlation filter prediction. The proposed semantics-contextual estimation method can generate more credible candidates. Experimental results show the state-of-the-art performance of our method. Abstract: In real world, most of the tracking methods suffer from uncertain motion, which may make the tracker failure because of a local search window with the motion smooth assumption. To address this problem, a novel tracking framework based on convolutional net with semantics estimation and region proposals is proposed. Firstly, we present a semantics object proposals generation strategy, including category-level semantics proposals, one-object-level semantics estimation and semantics-contextual proposals generation, to obtain a few of high-quality object-oriented proposals covering uncertain motion. Secondly, combining the globally sparse semantics region proposals prediction and correlation filter prediction, a hybrid semantics tracking algorithm is proposed, which obtains a coarse object location by the decision of multiple response maps. Finally, we learn and train independent correlation filter to estimate the scale of target for a higher tracking accuracy. Extensive experiments on two visual tracking benchmarks and results demonstrate our method achieves state-of-the-art performance.