Investigations of object detection in images/videos using various deep learning techniques and embedded platforms-A comprehensive review

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisReviewForskningpeer review


  • Chinthakindi Balaram Murthy, National Institute of Technology Warangal
  • ,
  • Mohammad Farukh Hashmi, National Institute of Technology Warangal
  • ,
  • Neeraj Dhanraj Bokde
  • Zong Woo Geem, Gachon University

In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola-Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.

TidsskriftApplied Sciences
StatusUdgivet - maj 2020

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