Smart Vision & Robotic Sensing

Professor, Robotics Laboratory
Smart Innovation Program, Graduate School of Advanced Science and Engineering
Hiroshima University
>> Research Contents
In order to establish high-speed robot senses that are much faster than human senses, we are conducting research and development of information systems and devices that can achieve real-time image processing at 1000 frames/s or greater. As well as integrated algorithms to accelerate sensor information processing, we are also studying new sensing methodologies based on vibration and flow dynamics; they are too fast for humans to sense.

Frame-Straddling-Based Optical Flow

In this study, 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 frame-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 frame-straddling 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 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 40ms 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.

punching motion
car on a street