Real-time optical flow estimation on a GPU for a skied-steered mobile robot

2016 ◽  
Author(s):  
V. V. Kniaz
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Real Time ◽  
Flow Estimation ◽  
Optical Flow Estimation

Real-Time Optical Flow Estimation Using Multiple Frame-Straddling Intervals

2012 ◽  
Vol 24 (4) ◽  
pp. 686-698 ◽  
Author(s):  
Lei Chen ◽  
◽  
Hua Yang ◽  
Takeshi Takaki ◽  
Idaku Ishii
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
High Speed ◽  
Frame Rate ◽  
Low Speed ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Optical Flows ◽  
Multiple Frame ◽  
Measurable Range

In this paper, we propose a novel method for accurate optical flow estimation in real time for both high-speed and low-speed moving objects based on High-Frame-Rate (HFR) videos. We introduce a multiframe-straddling function to select several pairs of images with different frame intervals from an HFR image sequence even when the estimated optical flow is required to output at standard video rates (NTSC at 30 fps and PAL at 25 fps). The multiframestraddling function can remarkably improve the measurable range of velocities in optical flow estimation without heavy computation by adaptively selecting a small frame interval for high-speed objects and a large frame interval for low-speed objects. On the basis of the relationship between the frame intervals and the accuracies of the optical flows estimated by the Lucas–Kanade method, we devise a method to determine multiple frame intervals in optical flow estimation and select an optimal frame interval from these intervals according to the amplitude of the estimated optical flow. Our method was implemented using software on a high-speed vision platform, IDP Express. The estimated optical flows were accurately outputted at intervals of 40 ms in real time by using three pairs of 512×512 images; these images were selected by frame-straddling a 2000-fps video with intervals of 0.5, 1.5, and 5 ms. Several experiments were performed for high-speed movements to verify that our method can remarkably improve the measurable range of velocities in optical flow estimation, compared to optical flows estimated for 25-fps videos with the Lucas–Kanade method.

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