Visual Object Tracking in RGB-D Data via Genetic Feature Learning

Ming-Xin Jiang,Tao Hai,Xian-Xian Luo,Ahmed N Abdalla,Hai-Yan Wang,Song Yang

doi:10.1155/2019/4539410

Abstract

Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.

Highlights

Visual object tracking, as a hot research topic in computer vision, has been potential applications including intelligent surveillance systems [1], sport analysis [2], advanced assistance systems [3], etc
The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves a higher accuracy and faster tracking speed
We have developed a RGB-D tracking method based on genetic feature learning, which can fuse the color and depth information for visual object tracking tasks

Summary

Introduction

As a hot research topic in computer vision, has been potential applications including intelligent surveillance systems [1], sport analysis [2], advanced assistance systems [3], etc. The best tracking performance was achieved by calculating HOG features on both color and depth data. The tracking methods introduced above are all based on handcrafted feature, which are usually designed by human experts and only achieve good performance in some particular domain. To overcome these limitations, deep learning features have been applied to RGB-D tracker. We propose a visual object tracking method in RGB-D data via genetic feature learning. To fuse the information in RGB and depth modality, the sum of errors between genetic feature of target object in frame t-1 and the candidates in current frame are computed. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves a higher accuracy and faster tracking speed

Proposed RGB-D Tracking Algorithm

56 HHA Encoding

Experiments and Results

Conclusion