Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video

Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video

T. Raghuveera (Anna University, Department of Computer Science and Engineering, Tamil Nadu, India), S. Vidhushini (Anna University, Department of Computer Science and Engineering, Tamil Nadu, India) and M. Swathi (Anna University, Department of Computer Science and Engineering, Tamil Nadu, India)
Copyright: © 2017 |Pages: 13
DOI: 10.4018/IJIIT.2017040104

Abstract

Real-Time Facial and eye tracking is critical in applications like military surveillance, pervasive computing, Human Computer Interaction etc. In this work, face and eye tracking are implemented by using two well-known methods, CAMSHIFT and RANSAC. In our first approach, a frontal face detector is run on each frame of the video and the Viola-Jones face detector is used to detect the faces. CAMSHIFT Algorithm is used in the real- time tracking along with Haar-Like features that are used to localize and track eyes. In our second approach, the face is detected using Viola-Jones, whereas RANSAC is used to match the content of the subsequent frames. Adaptive Bilinear Filter is used to enhance quality of the input video. Then, we run the Viola-Jones face detector on each frame and apply both the algorithms. Finally, we use Kalman filter upon CAMSHIFT and RANSAC and compare with the preceding experiments. The comparisons are made for different real-time videos under heterogeneous environments through proposed performance measures, to identify the best-suited method for a given scenario.
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Computer Vision plays an important role in many applications, especially in artificial intelligence and pervasive computing. The Continuously Adaptive Mean Shift Algorithm or CAMSHIFT (Arceneaux IV, Schedin, & White, 2010) is an adaptation of the Mean Shift algorithm for object tracking and is used for head and face tracking. Random sample consensus, or RANSAC (Martin & Robert, 1981), is an iterative method for estimating a mathematical model from a data set that contains outliers. The current procedures for face detection and tracking have many challenges such as malfunction due to pose Variations, lighting variation and shadowing, blur due to fast motion, occlusion and clutter, facial deformation, facial resolution in videos, quick and large in-plane and out-of-plane head rotation.

(Lu, Dai, & Hager, 2006) Showed a new object tracking approach based on the analysis of a two-dimensional image distribution histogram calculated from two colorimetric channels automatically selected on criteria of representativeness. This approach is a prolongation of the CAMSHIFT algorithm applications (continuously adaptive mean shift) in order to track object presenting strong modifications of shape and light. (Lae-Kyoung Lee, 2011) presented an approach to solve the problem of real-time face detection and tracking in a complex environment. The method consists of two phases.

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