NDLI: Feature and decision level fusion for action recognition

Content Provider	IEEE Xplore Digital Library
Author	Abouelenien, M. Yiwen Wan Saudagar, A.
Copyright Year	2012
Description	Author affiliation: Univ. of North Texas, Denton, TX, USA (Abouelenien, M.; Yiwen Wan; Saudagar, A.)
Abstract	Classification of actions by human actors from video enables new technologies in diverse areas such as surveillance and content-based retrieval. We propose and evaluate alternative models, one based on feature-level fusion and the second on decision-level fusion. Both models employ direct classification - inferring from low-level features the nature of the action. Interesting points are assumed to have salient 3D (spatial plus temporal) gradients that distinguish them from their neighborhoods. They are identified using three distinct 3D interesting-point detectors. Each detected interest point set is represented as a bag-of-words descriptor. The feature level fusion model concatenates descriptors subsequently used as input to a classifier. The decision level fusion uses an ensemble and majority voting scheme. Public data sets consisting of hundreds of action videos were used in testing. Within the test videos, multiple actors performed various actions including walking, running, jogging, handclapping, boxing, and waving. Performance comparison showed very high classification accuracy for both models with feature-level fusion having an edge. For feature-level fusion the novelty is the fused histogram of visual words derived from different sets of interesting points detected by different saliency detectors. For decision fusion besides Adaboost the majority voting scheme is also utilized in ensemble classifiers based on support vector machines, knearest neighbor, and decision trees. The main contribution, however, is the comparison between the models and, drilling down, the performance of different base classifiers, and different interest point detectors for human motion recognition
Sponsorship	IEEE Comput. Soc.
Starting Page	1
Ending Page	7
File Size	308116
Page Count	7
File Format	PDF
DOI	10.1109/ICCCNT.2012.6477851
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Visualization Histograms Accuracy Hidden Markov models Detectors Action classification; Feature-level fusion;Decision-level fusion; Adaboost; Direct classification Feature extraction
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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