NDLI: Action recognition by silhouette fusion

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Action recognition by silhouette fusion

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Action recognition by silhouette fusion

Content Provider	ACM Digital Library
Author	Saleem, Sehrish Rehman, Sajid Ur Naz, Samra Asad, Hafsa
Abstract	Vision based human activity or action recognition is an important application of video surveillance, human computer interaction, logistic support, location based services and many more. In this paper, an intelligent system is proposed that recognizes human actions from videos and classifies them using the technique of silhouette frame fusion. For a given video sequence, background is subtracted from each frame and human silhouette is obtained. All the silhouettes from the extracted frames are fused and Local Phase Quantization (LPQ) features are extracted from them. SVM proves to be the best classifier to solve this multiclass problem, categorizing each defined activity, by giving 91.6 % accuracy. This two fold contribution towards the action recognition techniques is unique and simple yet powerful for identifying activities from a video without compromising the computational time. This novel and robust system works even if complete silhouette is not extracted- thus making it a practical approach that can be used in real time activity recognition.
Starting Page	127
Ending Page	131
Page Count	5
File Format	PDF
ISBN	9781450348577
DOI	10.1145/3056662.3056706
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Action recognition Support vector machines Surveillance Neural networks Feature extraction Artificial intelligence
Content Type	Text
Resource Type	Article

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