Visual Tracking by Sampling in Part Space

Date

2017

Authors

Huang, Lianghua
Ma, Bo
Shen, Jianbing
He, Hui
Shao, Ling
Porikli, Fatih

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods.

Description

Keywords

Visual tracking, part space, sampling

Citation

Source

IEEE Transactions on Image Processing

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

Restricted until

2099-12-31