Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

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Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow

2018 25th IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2018.8451406 ◽

2018 ◽

Cited By ~ 2

Author(s):

Sarthak Pathak ◽

Alessandro Moro ◽

Hiromitsu Fujii ◽

Atsushi Yamashita ◽

Hajime Asama

Keyword(s):

Motion Estimation ◽

Optical Flow ◽

Camera Motion ◽

Dense Optical Flow ◽

Camera Motion Estimation ◽

Spherical Camera

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

Sensors ◽

10.3390/s19040807 ◽

2019 ◽

Vol 19 (4) ◽

pp. 807

Author(s):

Cong Shi ◽

Zhuoran Dong ◽

Shrinivas Pundlik ◽

Gang Luo

Keyword(s):

Embedded System ◽

Optical Flow ◽

Random Forests ◽

Visual Motion ◽

Hardware Acceleration ◽

Ground Truth ◽

Estimation Algorithm ◽

Estimation Accuracy ◽

Time To Collision ◽

Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

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Efficient sparse-to-dense optical flow estimation using a learned basis and layers

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr.2015.7298607 ◽

2015 ◽

Cited By ~ 59

Author(s):

Jonas Wulff ◽

Michael J. Black

Keyword(s):

Optical Flow ◽

Flow Estimation ◽

Optical Flow Estimation ◽

Dense Optical Flow

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Optical Flow Estimation with Occlusion Detection

Algorithms ◽

10.3390/a12050092 ◽

2019 ◽

Vol 12 (5) ◽

pp. 92

Author(s):

Song Wang ◽

Zengfu Wang

Keyword(s):

Optical Flow ◽

Feature Matching ◽

Estimation Algorithm ◽

Occlusion Detection ◽

Multiple Sources ◽

Flow Estimation ◽

Optical Flow Estimation ◽

Dense Optical Flow ◽

Video Frames ◽

Optimization Function

The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.

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