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Communication Dans Un Congrès Année : 2017

Multi-object Tracking Using Compressive Sensing Features in Markov Decision Process

Résumé

In this paper, we propose an approach which uses compressive sensing features to improve Markov Decision Process (MDP) tracking framework. First, we design a single object tracker which integrates compressive tracking into Tracking-Learning-Detection (TLD) framework to complement each other. Then we apply this tracker into the MDP tracking framework to improve the multi- object tracking performance. A discriminative model is built for each object and updated online. With the built discriminative model, the features used for data association are also enhanced. In order to validate our method, we first test the designed single object tracker with a common dataset. Then we use the validation set from the multiple object tracking (MOT) training dataset to analyze each part of our method. Finally, we test our approach in the MOT benchmark. The results show our approach improves the original method and performs superiorly against several state-of-the-art online multi-object trackers.
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Dates et versions

hal-01695853 , version 1 (29-01-2018)

Identifiants

Citer

Tao Yang, Cindy Cappelle, Yassine Ruichek, Mohammed El Bagdouri. Multi-object Tracking Using Compressive Sensing Features in Markov Decision Process. Advanced Concepts for Intelligent Vision Systems (ACIVS'2017), Springer Lecture Notes in Computer Science (LNCS), vol. 10617, 2017, Antwerp, Belgium. pp.505-517, ⟨10.1007/978-3-319-70353-4_43⟩. ⟨hal-01695853⟩
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