Kernel Based Multiple Cue Adaptive Appearance Model For Robust Real-time Visual Tracking

In this letter, we propose a robust and real-time visual tracking algorithm via a novel kernel based multiple cue adaptive appearance model (KBMCAAM). In particular, the appearance model is constructed with a naive Bayes classifier which is trained utilizing sparse multi-scale Haar-like features weighted by a spatial kernel function. Moreover, multiple image cues are integrated to improve the model's discriminative capacity. Experimental results demonstrate the superior performance of our proposed method to many state-of-art algorithms.

I. Introduction

Visual tracking is a challenging problem within the field of computer vision due to cluttering background, similar objects and appearance changes caused by pose, illumination, occlusion and motion. Additionally, real-time processing requirement of applications such as intelligent visual surveillance, augmented reality, robot vision, makes it harder.