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Output details

13 - Electrical and Electronic Engineering, Metallurgy and Materials

University of Surrey

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Output 39 of 311 in the submission

Article title

Action Recognition Using Mined Hierarchical Compound Features

Type

D - Journal article

DOI

10.1109/TPAMI.2010.144

Title of journal

IEEE Transactions on Pattern Analysis and Machine Intelligence

Article number

Volume number

Issue number

First page of article

883

ISSN of journal

2160-9292

Year of publication

2011

URL

Number of additional authors

Additional information

This paper introduces a new approach to categorising/recognising actions in video data. Microsoft Kinect and the Video Google search engine are two examples of the potential value of methods for categorising and recognising actions in video data. This proposed work utilises machine-learning techniques and applies these to simple features to build larger distinctive structures. This work is evaluated on large scale, real-world datasets and out-perform state-of-the-art methods by a significant margin. The research is in the highest-ranking journal in the field and is becoming highly cited.

Interdisciplinary

Cross-referral requested

Research group

None

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract