Distinctive action sketch for human action recognition

Date

2018

Authors

Zheng, Ying
Yao, Hongxun
Sun, Xiaoshuai
Zhao, Sicheng
Porikli, Fatih

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

Recent developments in the field of computer vision have led to a renewed interest in sketch correlated research. There have emerged considerable solid evidence which revealed the significance of sketch. However, there have been few profound discussions on sketch based action analysis so far. In this paper, we propose an approach to discover the most distinctive sketches for action recognition. The action sketches should satisfy two characteristics: sketchability and objectiveness. Primitive sketches are prepared according to the structured forests based fast edge detection. Meanwhile, we take advantage of Faster R-CNN to detect the persons in parallel. On completion of the two stages, the process of distinctive action sketch mining is carried out. After that, we present four kinds of sketch pooling methods to get a uniform representation for action videos. The experimental results show that the proposed method achieves impressive performance against several compared methods on two public datasets.

Description

Keywords

Citation

Source

Signal Processing

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

DOI

10.1016/j.sigpro.2017.10.022

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