NDLI: Detecting salient fragments for video human action detection and recognition using an associative memory

Content Provider	IEEE Xplore Digital Library
Author	Da-Wei Kuo Guan-Yu Cheng Shyi-Chyi Cheng Su-Ling Lee
Copyright Year	2012
Description	Author affiliation: Department of Computer Science & Engineering, National Taiwan Ocean University Keelung, Taiwan (Da-Wei Kuo; Guan-Yu Cheng; Shyi-Chyi Cheng) \|\| Department of Computer Sci. & Information Engi., Chang-Jung Christian University, Tainan, Taiwan (Su-Ling Lee)
Abstract	This paper presents a novel approach to locate action objects in video and recognize their action types simultaneously using an associative memory model. The system uses a preprocessing procedure to extract key-frames from a video sequence and provide a compact representation for this video. Every training key-frame is partitioned into multiple overlapping patches in which image and motion features are extracted to generate an appearance-motion codebook. The training procedure also constructs a two-directional associative memory based on the learnt codebook to facilitate the system detecting and recognizing video action events using salient fragments, patch groups with common motion vectors. Our approach proposes the recently-developed Hough voting model as a framework for human action learning and memory. For each key-frame, the Hough voting framework employs Generalized Hough Transform (GHT) which constructs a graphical structure based on key-frame codewords to learn the mapping between action objects and a Hough space. To determine which patches explicitly represent an action object, the system detects salient fragments whose member patches are used to infer the associative memory and retrieve matched patches from the Hough model. These model patches are then used to locate the target action object and classify the action type simultaneously using a probabilistic Hough voting scheme. Results show that the proposed method gives good performance on several publicly available datasets in terms of detection accuracy and recognition rate.
Starting Page	1039
Ending Page	1044
File Size	785736
Page Count	6
File Format	PDF
ISBN	9781467311564
e-ISBN	9781467311571
e-ISBN	9781467311557
DOI	10.1109/ISCIT.2012.6380844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-02
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Shape Video sequences recognition Vectors Action shapes Generalized Hough Transform Training Associative memory assicition memory humen action detection Feature extraction salient fragment
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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