Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2016

Abstract

Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point (STIP) based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words (BoVW) model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the “directional co-occurrence” property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the “geometric relationships” between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature (TSP) and the Space Salient Pairwise feature (SSP) are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP + SSP + BoVW can properly describe human actions with large intro-variability in real-time.

Keywords

Spatio-temporal interest point, Bag-of-visual words, Co-occurrence

Discipline

Computer Engineering | Software Engineering

Research Areas

Data Science and Engineering

Publication

CAAI Transactions on Intelligence Technology

Volume

1

Issue

1

First Page

14

Last Page

29

ISSN

2468-2322

Identifier

10.1016/j.trit.2016.03.001

Publisher

Institution of Engineering and Technology

Additional URL

https://doi.org/10.1016/j.trit.2016.03.001

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