Front Inner Page - Volume 2 No.6 December 2015

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  • Title

    :

    An Efficient Activity Tracking and Recognition Using the Neural Network Classifier

    Authors

    :

    G.Karthic1, B.Lalitha2

    Keywords

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    Local Binary Pattern (LBP), Neural Network (NN), Support Vector Machine (SVM), Hierarchical Markov Random Field-Sparce (HMRF-Sparce) technique, Bounding box technique, Gaussian filter.

    Issue Date

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    December – 2015

    Abstract

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    A continuous video contains two important components such as tracks of the person in the video, and localization of the actions that are performed by the actors. The analysis of the activity is used for solving both the tracking, and recognition problems. In this paper, we have deployed an efficient activity analysis framework for determining the activity of the human in the video. Initially, the input video is obtained from the ULCA, and VIRAT datasets, then the video file is converted into multiple video frames named as frames. The information regarding each frames are obtained and further the frames are resized for preventing the memory from dumping. The noise present in each frame is filtered using the Gaussian filter. The Hierarchical Markov Random Field-Sparce (HMRF-Sparce) technique is used for extracting the shape of the object from the background. The tracking of the video file is performed using the Bounding Box technique. The features from the resultant image are extracted using the Local Binary Pattern (LBP). Based on the features obtained the frames a pattern is generated. These feature values are grouped into activity segments using the Neural Network (NN) classifier. To validate the performance of the proposed NN classifier it is validated with the existing Support Vector Machine (SVM) classifier for the metrics such as accuracy, precision, and recall. The experimental results proved that the proposed NN classifier produced optimal results than the existing SVM.  

    Page(s)

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    6-10

    ISSN

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    2347- 4734

    Source

    :

    Vol. 2, No.6, December 2015

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