Tracking-Learning-Detection using Extreme Learning Machine with Haar-Like Features

Abstract

Nowadays, the applications of tracking moving object are commonly used in various areas especially in computer vision applications. There are many tracking algorithms have been introduced and they are divided into three groups which are generative trackers, discriminative trackers and hybrid trackers. One of the methods is Tracking Learning-Detection (TLD) framework which is an example of the hybrid trackers where combination between the generative trackers and the discriminative trackers occur. In TLD, the detector consists of three stages which are patch variance, ensemble classifier and KNearest Neighbor classifier. In the second stage, the ensemble classifier depends on simple pixel comparison hence, it is likely fail to offer a better generalization of the appearances of the target object in the detection process. In this paper, Online Sequential Extreme Learning Machine (OS-ELM) was used to replace the ensemble classifier in the TLD framework. Besides that, different types of Haar-like features were used for the feature extraction process instead of using raw pixel value as the features. The objectives of this study are to improve the classifier in the second stage of detector in TLD framework by using Haar-like features as an input to the classifier and to get a more generalized detector in TLD framework by using OS-ELM based detector. The results showed that the proposed method performs better in Pedestrian 1 in terms of F-measure and also offers good performance in terms of Precision in four out of six videos.