Real Time Object Detection using CNN

  • Authors

    • Akash Tripathi
    • T V. Ajay Kumar
    • Tarun Kanth Dhansetty
    • J Selva Kumar
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.11994
  • Convolution Neural Network(CNN), Scale-Invariant Feature Transform(SIFT), confidence value, object detection, proposed regions.

  • Abstract

    Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulation results show better performance in accuracy when compared with the conventional CNN method. As the resources like RAM, graphic card, ROM, etc. are limited we propose a pipelined implementation on an aggregate Central Processing Unit(CPU) and Graphical Processing Unit(GPU) platform.

     

     

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  • How to Cite

    Tripathi, A., V. Ajay Kumar, T., Kanth Dhansetty, T., & Selva Kumar, J. (2018). Real Time Object Detection using CNN. International Journal of Engineering & Technology, 7(2.24), 33-36. https://doi.org/10.14419/ijet.v7i2.24.11994

    Received date: 2018-04-24

    Accepted date: 2018-04-24

    Published date: 2018-04-25