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

Sensors ◽  
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.

scholarly journals Optical Flow Estimation with Occlusion Detection

Algorithms ◽  
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.

Visual motion perception for mobile robots through dense optical flow fields

2017 ◽  
Vol 87 ◽  
pp. 1-14 ◽  
Author(s):  
Andry Maykol Pinto ◽  
Paulo G. Costa ◽  
Miguel V. Correia ◽  
Anibal C. Matos ◽  
A. Paulo Moreira
Keyword(s):  
Mobile Robots ◽  
Optical Flow ◽  
Motion Perception ◽  
Visual Motion ◽  
Flow Fields ◽  
Visual Motion Perception ◽  
Dense Optical Flow

scholarly journals Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2164
Author(s):  
Anis Ammar ◽  
Hana Ben Fredj ◽  
Chokri Souani
Keyword(s):  
Motion Estimation ◽  
Embedded System ◽  
Optical Flow ◽  
Local Treatment ◽  
Raspberry Pi ◽  
Apparent Velocity ◽  
Flow Estimation ◽  
Dense Optical Flow ◽  
Conventional Calculation ◽  
Estimation 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.

MODIFIED METHOD FOR REAL-TIME COMPUTING DENSE OPTICAL FLOW ON FPGA

2019 ◽  
Vol 70 ◽  
pp. 146-157
Author(s):  
A. V. Bratulin ◽  
◽  
M. B. Nikiforov ◽  
A. I. Efimov ◽  
◽  
...  
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Modified Method ◽  
Dense Optical Flow ◽  
Real Time Computing

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

scholarly journals Experimental Comparison between Event and Global Shutter Cameras

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1137
Author(s):  
Ondřej Holešovský ◽  
Radoslav Škoviera ◽  
Václav Hlaváč ◽  
Roman Vítek
Keyword(s):  
High Speed ◽  
Ground Truth ◽  
Motion Blur ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Experimental Comparison ◽  
Estimation Errors ◽  
Detection Rates ◽  
Ballistic Experiment ◽  
Event Camera

We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

Image Stabilization Based on Panoramic Dense Optical Flow Matching for Camera on Flapping Wing

2021 ◽  
Author(s):  
Tian Shen ◽  
Cui Long ◽  
Liu Zhaoming ◽  
Wang Hongwei ◽  
Zhang Feng ◽  
...  
Keyword(s):  
Optical Flow ◽  
Flapping Wing ◽  
Image Stabilization ◽  
Dense Optical Flow
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