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A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm.

Cong Shi1,2, Zhuoran Dong3, Shrinivas Pundlik4

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China. shicong@semi.ac.cn.

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|February 21, 2019
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Summary
This summary is machine-generated.

This study presents a hardware-efficient algorithm for estimating Time-to-Collision (TTC) using dense optical flow on smart sensors. It enables reliable collision avoidance by analyzing motion, offering scalable performance for embedded systems.

Keywords:
biological visual featuresmotion energymotion estimationoptical flowspatiotemporal energytime-to-collision

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Area of Science:

  • Computer Vision
  • Robotics
  • Embedded Systems

Background:

  • Collision avoidance systems require accurate real-time estimation of Time-to-Collision (TTC).
  • Existing methods may not be suitable for hardware deployment on resource-constrained smart sensors.
  • Dense optical flow analysis is a promising approach for motion perception in vision systems.

Purpose of the Study:

  • To propose a hardware-friendly, dense optical flow-based algorithm for TTC estimation.
  • To enable deployment on smart video sensors for real-time collision avoidance applications.
  • To achieve a balance between estimation accuracy, processing speed, and hardware resource utilization.

Main Methods:

  • Extraction of biological visual motion features (motion energies).
  • Prediction of dense optical flow using a Random Forests regressor.
  • TTC estimation from the divergence of the optical flow field.

Main Results:

  • The proposed algorithm demonstrates improved optical flow estimation accuracy compared to voting-based methods.
  • Estimated TTC values closely align with ground truth data.
  • The algorithm's feed-forward, pixel-level operations facilitate hardware acceleration and scalability.

Conclusions:

  • The developed algorithm is suitable for real-time embedded systems, offering a hardware-friendly solution for collision avoidance.
  • The use of Random Forests enhances optical flow prediction robustness and accuracy.
  • Flexible tradeoffs between accuracy, speed, and hardware resources are achievable.