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Real-Time Embedded Object Tracking with Discriminative Correlation Filters Using Convolutional Features

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Applied Reconfigurable Computing. Architectures, Tools, and Applications (ARC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13569))

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Abstract

Object tracking is an essential element of visual perception systems. It is used in advanced video surveillance systems (AVSS), autonomous vehicles, robotics, and many more. For applications such as autonomous robots, the system must be implemented on some embedded platform with limited computing performance and power. Furthermore, sufficiently fast response is required from the tracking system in order to perform some real-time tasks. Discriminative Correlation Filter (DCF) based tracking algorithms are popular for such applications, as they offer state-of-the-art performance while not being too computationally complex. In this paper, an FPGA implementation of the DCF tracking algorithm using convolutional features is presented. The ZCU104 board is used as a platform, and the performance is evaluated on the VOT2015 dataset. In contrast to other implementations that use HOG (Histogram of Oriented Gradients) features, this implementation achieves better results for \(64 \times 64\) filter size while being able to potentially operate at higher speeds (over 467 fps per scale).

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Notes

  1. 1.

    In previous FINN versions, Alveo boards were also supported (up to v0.7).

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Acknowledgment

The work presented in this paper was supported by the National Science Centre project no. 2016/23/D/ST6/01389 entitled “The development of computing resources organisation in latest generation of heterogeneous reconfigurable devices enabling real-time processing of UHD/4K video stream” and AGH University of Science and Technology project no. 16.16.120.773.

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Authors and Affiliations

  1. Embedded Vision Systems Group, Computer Vision Laboratory, Department of Automatic Control and Robotics, AGH University of Science and Technology, Krakow, Poland

    Michal Danilowicz & Tomasz Kryjak

Authors
  1. Michal Danilowicz
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  2. Tomasz Kryjak
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Correspondence to Tomasz Kryjak .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lin Gan

  2. Tsinghua University, Beijing, China

    Yu Wang

  3. Tsinghua University, Beijing, China

    Wei Xue

  4. Samsung AI Center, Cambridge, UK

    Thomas Chau

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Danilowicz, M., Kryjak, T. (2022). Real-Time Embedded Object Tracking with Discriminative Correlation Filters Using Convolutional Features. In: Gan, L., Wang, Y., Xue, W., Chau, T. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2022. Lecture Notes in Computer Science, vol 13569. Springer, Cham. https://doi.org/10.1007/978-3-031-19983-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-19983-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19982-0

  • Online ISBN: 978-3-031-19983-7

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