A Method for Estimating Motions of Contours with an Application to Gait Recognition

A METHOD FOR ESTIMATING MOTIONS OF CONTOURS WITH AN APPLICATION TO GAIT RECOGNITION Sam Gelman George Mason University, 2016 Thesis Director: Dr. Zoran Durić In this thesis, I propose a novel method for estimating motions between image contours. The method is fast and can handle both small and large displacements. It uses the distance transform and its gradients to estimate correspondences between points. The distance transform of a binary contour represents the distance of each pixel to the nearest contour pixel. The gradient of the distance transform points in the direction normal to the contour. By combining unit vectors obtained from the gradient with the original distance transform, the method produces vectors that correspond to the normal displacement between pairs of contours. This method can then be extended to compute true motion near corners as well as parameterized motion models. Experiments on various shape contours show the method’s efficacy in computing normal displacements. Cases that do not correspond to normal motion are analyzed and corrected through the use of contour normals and motion models. The method is also applied to gait recognition, the goal of which is to identify people in videos based on their unique walking pattern. Many gait recognition methods operate on sequences of binary silhouettes. Since contours are easily obtainable from silhouettes, the proposed method is well-suited to the task. I describe two representations, the Histogram of Motion and the Edge Motion Vector, that allow for the comparison of contour motion between frames and sequences. These representations are tested on a large gait recognition database and achieve rank 1 and rank 5 performance that is comparable to the state of the art. The success of the method on gait recognition shows it is useful as a standalone representation, but it can also be used to improve other techniques and for other applications this is left to future work. Chapter 1: Introduction The goal of motion estimation is to compute displacement between corresponding features in pairs of images. Motion estimation has a long history of research dating back to the seminal works of Horn and Schunck [1] and Lucas and Kanade [2]. It has been used in a wide range of application domains including video editing, action recognition, and person identification. The motions of interest in most applications are the true motions of objects, e.g. people, cars, robots. However, actual motion in three-dimensional space does not necessarily correspond to apparent motion in two-dimensional images. Apparent motion can be caused by a variety of factors including the changes in the lighting of a scene. Motion estimation techniques are therefore concerned with finding the apparent projection in the two-dimensional image of the actual motion in a three-dimensional scene. Motion estimation techniques can be roughly divided into two categories: dense and sparse. Dense techniques are concerned with finding a motion vector for each pixel in an image. The resulting motion field is referred to as optical flow. These techniques establish correspondences between pixel regions by assuming that the intensity of pixel regions remains constant during displacement. This assumption is only valid for small translational displacements. Conversely, sparse techniques are concerned with finding motion vectors between a small number of select features. Once features are matched, displacement can be computed for arbitrarily large distances. Some recent work on motion estimation has focused on combining dense and sparse techniques. Motion can also be represented parametrically based on motion vectors computed from dense or sparse techniques. Such representations are useful for handling errors and compactly describing motion. Parametric motion models are subject to a trade-off between complexity and representativeness. A good balance between these factors usually limits