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Generic Object Detection Framework with Spatially Pooled Features

  • K. VenkatachalapathyEmail author
  • K. Kishore Anthuvan Sahayaraj
  • V. Ohmprakash
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)

Abstract

Generic Object detection technique plays an important role in the traffic surveillance and security-related issues. Research has been done over the past several years and accomplished great progresses via convolutional neural network (CNN) which has greatly enhanced the performance in image classification and object detection. This proposal is similar to the notion of R-CNN [1], presents a novel method that combines the spatially pooled features (sp-Cov) as a part of aggregated channel (ACF) and CNN for object detection. The proposed technique uses sp-Cova and ACF to select the possible object on interest regions and then trains a CNN model to filter out non-face candidates. Merging the results of sp-Cov + ACF and CNN to get the final detection window(s). The proposed framework achieves the good performance with state-of-the-art methods on numerous benchmark data sets.

Keywords

Object detection CNN Neural networks 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • K. Venkatachalapathy
    • 1
    Email author
  • K. Kishore Anthuvan Sahayaraj
    • 1
  • V. Ohmprakash
    • 1
  1. 1.Annamalai UniversityChidambaramIndia

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