• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Convolutional Neural Network with Particle Filter Approach for Visual Tracking

Vol. 12, No.2, February 28, 2018
10.3837/tiis.2018.02.009, Download Paper (Free):

Abstract

In this paper, we propose a compact Convolutional Neural Network (CNN)-based tracker in conjunction with a particle filter architecture, in which the CNN model operates as an accurate candidates estimator, while the particle filter predicts the target motion dynamics, lowering the overall number of calculations and refines the resulting target bounding box. Experiments were conducted on the Online Object Tracking Benchmark (OTB) [34] dataset and comparison analysis in respect to other state-of-art has been performed based on accuracy and precision, indicating that the proposed algorithm outperforms all state-of-the-art trackers included in the OTB dataset, specifically, TLD [16], MIL [1], SCM [36] and ASLA [15]. Also, a comprehensive speed performance analysis showed average frames per second (FPS) among the top-10 trackers from the OTB dataset [34].


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Cite this article

[IEEE Style]
Vladimir Tyan1 and Doohyun Kim, "Convolutional Neural Network with Particle Filter Approach for Visual Tracking," KSII Transactions on Internet and Information Systems, vol. 12, no. 2, pp. 693-709, 2018. DOI: 10.3837/tiis.2018.02.009

[ACM Style]
Tyan1, V. and Kim, D. 2018. Convolutional Neural Network with Particle Filter Approach for Visual Tracking. KSII Transactions on Internet and Information Systems, 12, 2, (2018), 693-709. DOI: 10.3837/tiis.2018.02.009