Abstract
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.
Highlights
Unlike the processing of static images, much broader information can be extracted from time varying image sequences, this being one of the primary functions of a computer vision system.Obtaining motion information is a challenging task for machines, several techniques have been developed in order to obtain the requested motion field
We have examined the performance of iterative Lucas-Kanade pyramidal optical flow algorithm together with different filtering techniques using well-known image sequences, provided with ground truth optical flow
We have measured the performance of the estimated optical flow using both average angular error (AAE) and average endpoint error (AEE)
Summary
Unlike the processing of static images, much broader information can be extracted from time varying image sequences, this being one of the primary functions of a computer vision system. Many different optical flow algorithms have been developed since 1981, including extensions and modifications of the Horn-Schunck and Lucas-Kanade approaches. The authors employed one filtering method in the evaluation of optical flow. In order to measure the concentration field of an injected gaseous fuel, Iffa et al [12] employ a pyramidal Lucas-Kanade flow determination in conjunction with a 5 × 5 kernel Gaussian filter. We focused on improving the accuracy of optical flow estimation by using the appropriate filtering method. As image filtering is essential in many applications, including smoothing, noise removal or edge detection, in the case of optical flow, we have investigated the filtering technique as a required preprocessing step.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
