KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Convolutional Neural Network with Particle Filter Approach for Visual Tracking

  • Received : 2017.07.04
  • Accepted : 2017.08.11
  • Published : 2018.02.28
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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].

Keywords

References

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  1. An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering vol.4, pp.4, 2018, https://doi.org/10.3390/bdcc4040027