In this paper, we propose a method called superpixel tensor pooling tracker which can fuse multiple midlevel cues captured by superpixels into sparse pooled tensor features. Our method first adopts the superpixel method to generate different patches (superpixels) from the target template or candidates. Then for each superpixel, it encodes different midlevel cues including HSI color, RGB color, and spatial coordinates into a histogram matrix to construct a new feature space. Next, these matrices are formed to a third order tensor. After that, the tensor is pooled into the sparse representation. Then the incremental positive and negative subspaces learning is performed. Our method has both good characteristics of midlevel cues and sparse representation hence is more robust to large appearance variations and can capture compact and informative appearance of the target object. To validate the proposed method, we compare it with state-ofthe-art methods on 24 sequences with multiple visual tracking challenges. Experiment results demonstrate that our method outperforms them significantly.

Introduction

The study of visual tracking has been achieved great successes in recent years. Visual tracking is a process of locating a moving object or multiple objects over time in a video stream or using a camera. It can be divided into three steps: (1) object detection; (2) location prediction; (3) data association. Before using tracking algorithm to perform these steps, for each video application, a shot boundary detection needs to be performed to extract the sequence [1]. However, because of the heavy occlusion, drifts, fast motion, severe scale variation, large shape deformation, etc., visual tracking is still a challenge in computer vision [2]–[۴]. Many advanced visual tracking methods have been developed to solve these challenges, such as sparse representation based approaches, correlation filter (CF) based methods, deep learning (DL) based methods, etc. Sparse representation has been introduced successfully into the construction of the appearance model in visual tracking [3]–[۵]. It uses the sparse linear representation to represent the candidates [3], [5]. It can use very few but most related target templates to reduce impacts of background noise [4]. Moreover, it can use local sparse codes to model the target appearance adaptively and exploit the discriminative nature [4].