Densely sampled noiseless optical flow for motion based visual activity analysis

Abstract

Video based activity analysis is quite interesting area of research in computer vision and machine learning community due to having great impact for solving video surveillance, system monitoring and social media analytics problems. Optical flow estimation provides a novel benchmark for motion based human activity analysis in video sequences. Optical flow is a method to reflect the changes between two image sequences due to the variation space and time parameters of the objects. Varying motion parameters in an image sequence, makes harder to compute dense flow in the optical flow of pixels. Determining optical flow is easier by Horn-Shunck and Lucas-Kanade methods due to its dependency on similarity in nature of reflected light from both the images. Dense optical flow is assured to smooth by Horn-Shunck methods but lacking the neighboring pixel information. For noise removal Lucas-Kanade method is successful but due to small range of velocity, it fails to provide dense optical flow. In this work, by pointing these issues we introduced the smoothness constraint to find the grey level corners and smooth the optical flow across edges. Finally we combine this approach with Nagel and Horn-Shunck methods to get dense and noiseless optical flow. This approach gives promising results for smooth optical flow by preserving discontinuity at corners where pixel velocity sharply changes.