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Low-Cost Hardware-Accelerated Vision-Based Depth Perception for Real-Time Applications

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

Depth estimation and 3D object detection are critical for autonomous systems to gain context of their surroundings. In recent times, compute capacity has improved tremendously, enabling computer vision and AI on the edge. In this paper, we harness the power of CUDA and OpenMP to accelerate ELAS (a stereoscopic vision-based disparity calculation algorithm) and 3D projection of the estimated depth while performing object detection and tracking. We also examine the utility of Bayesian inference in achieving real-time object tracking. Finally, we build a drive-by-wire car equipped with a stereo camera setup to test our system in the real world. The entire system has been made public and easily accessible through a Python module.

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Authors and Affiliations

  1. Department of Computer Science, PES University, Bengaluru, Karnataka, 560085, India

    N. G. Aditya, P. B. Dhruval, S. S. Shylaja & Srinivas Katharguppe

Authors
  1. N. G. Aditya
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  2. P. B. Dhruval
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  3. S. S. Shylaja
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  4. Srinivas Katharguppe
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Corresponding author

Correspondence to N. G. Aditya .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Aditya, N.G., Dhruval, P.B., Shylaja, S.S., Katharguppe, S. (2023). Low-Cost Hardware-Accelerated Vision-Based Depth Perception for Real-Time Applications. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_22

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