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Fully Autonomous UAV-Based Action Recognition System Using Aerial Imagery

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12509))

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Abstract

Human action recognition is an important topic in artificial intelligence with a wide range of applications including surveillance systems, search-and-rescue operations, human-computer interaction, etc. However, most of the current action recognition systems utilize videos captured by stationary cameras. Another emerging technology is the use of unmanned ground and aerial vehicles (UAV/UGV) for different tasks such as transportation, traffic control, border patrolling, wild-life monitoring, etc. This technology has become more popular in recent years due to its affordability, high maneuverability, and limited human interventions. However, there does not exist an efficient action recognition algorithm for UAV-based monitoring platforms. This paper considers UAV-based video action recognition by addressing the key issues of aerial imaging systems such as camera motion and vibration, low resolution, and tiny human size. In particular, we propose an automated deep learning-based action recognition system which includes the three stages of video stabilization using the SURF feature selection and Lucas-Kanade method, human action area detection using faster region-based convolutional neural networks (R-CNN), and action recognition. We propose a novel structure that extends and modifies the InceptionResNet-v2 architecture by combining a 3D CNN architecture and a residual network for action recognition. We achieve an average accuracy of 85.83% for the entire-video-level recognition when applying our algorithm to the popular UCF-ARG aerial imaging dataset. This accuracy significantly improves upon the state-of-the-art accuracy by a margin of 17%.

This material is based upon the work supported by the National Science Foundation under Grant No. 1755984. This work is also partially supported by the Arizona Board of Regents (ABOR) under Grant No. 1003329.

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Authors and Affiliations

  1. Northern Arizona University, Flagstaff, AZ, 86011, USA

    Han Peng & Abolfazl Razi

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  1. Han Peng
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  2. Abolfazl Razi
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Correspondence to Abolfazl Razi .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Peng, H., Razi, A. (2020). Fully Autonomous UAV-Based Action Recognition System Using Aerial Imagery. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-64556-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64555-7

  • Online ISBN: 978-3-030-64556-4

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