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Object detection and recognition using contour based edge detection and fast R-CNN

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
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Abstract

Object detection is a technique of computer vision whose primary intent is to detect objects. The objects can be detected from any image or video feeds. Now a day’s object detection is extensively applied in video surveillance systems, human tracking, and self-driving cars. This paper presented a novel object detection approach that uses only wireframe-based features. The wireframe of the image is identified by using Cellular logical array processing. This technique can determine the visual and geometric features of the image. This paper focuses on a deep neural network framework to detect the target object in the image. Fast R-CNN is used for the detection of objects. The detection speed is fast because only the wireframe of the image is obtained first and then fed into the Fast RCNN model for detection and classification purposes. The performance of the proposed methodology is evaluated on PASCAL VOC, example-based synthesis dataset and real-time dataset. The proposed methodology gives mean average precision (mAP) 89.4%, 91.33% and 88.1% on PASCAL VOC, example-based and real-time dataset. The experimental analysis demonstrated that our proposed detection method achieves better results than the other state of art methods. The approach is helpful to detect the 2D and 3D objects as well.

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Authors and Affiliations

  1. Lovely Professional University, Punjab, India

    Shilpa Rani

  2. ECE Department, Lovely Professional University, Punjab, India

    Deepika Ghai

  3. ECE Department, Sreyas Institute of Engineering & Technology, Hyderabad, India

    Sandeep Kumar

Authors
  1. Shilpa Rani
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  2. Deepika Ghai
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  3. Sandeep Kumar
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Correspondence to Shilpa Rani.

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Rani, S., Ghai, D. & Kumar, S. Object detection and recognition using contour based edge detection and fast R-CNN. Multimed Tools Appl 81, 42183–42207 (2022). https://doi.org/10.1007/s11042-021-11446-2

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  • DOI: https://doi.org/10.1007/s11042-021-11446-2

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