@InProceedings{10.1007/978-981-19-7867-8_22,
  author="Aditya, N. G.
  and Dhruval, P. B.
  and Shylaja, S. S.
  and Katharguppe, Srinivas",
  editor="Tistarelli, Massimo
  and Dubey, Shiv Ram
  and Singh, Satish Kumar
  and Jiang, Xiaoyi",
  title="Low-Cost Hardware-Accelerated Vision-Based Depth Perception for Real-Time Applications",
  booktitle="Computer Vision and Machine Intelligence",
  year="2023",
  publisher="Springer Nature Singapore",
  address="Singapore",
  pages="271--282",
  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.",
  isbn="978-981-19-7867-8"
}