You are in:Home/Publications/Q. Abbas, Mostafa E.A. Ibrahim, and M. Jaffar. A comprehensive review of recent advances on deep vision systems. Springer Artificial Intelligence Review (Impact Factor 2017: 3.814) (2018). https://doi. 10.1007/s10462-018-9633-3

Dr. Mostafa Elsayed Ahmed Ibrahim :: Publications:

Title:
Q. Abbas, Mostafa E.A. Ibrahim, and M. Jaffar. A comprehensive review of recent advances on deep vision systems. Springer Artificial Intelligence Review (Impact Factor 2017: 3.814) (2018). https://doi. 10.1007/s10462-018-9633-3
Authors: Mostafa E.A. Ibrahim; Q. Abbas; M. Jaffar
Year: 2019
Keywords: Computer vision; Video processing; Object detection; Object tracking; Object recognition; Deep learning; Convolutional neural network; Deep belief network; Deep residual learning
Journal: Artificial Intelligence Review
Volume: 52
Issue: Not Available
Pages: 39–76
Publisher: Springer
Local/International: International
Paper Link:
Full paper Mostafa Elsayed Ahmed Ibrahim_A Comperhensive-10.1007_s10462-018-9633-3.pdf
Supplementary materials Not Available
Abstract:

Real-time video objects detection, tracking, and recognition are challenging issues due to the real-time processing requirements of the machine learning algorithms. In recent years, video processing is performed by deep learning (DL) based techniques that achieve higher accuracy but require higher computations cost. This paper presents a recent survey of the state-of-the-art DL platforms and architectures used for deep vision systems. It highlights the contributions and challenges from over numerous research studies. In particular, this paper first describes the architecture of various DL models such as AutoEncoders, deep Boltzmann machines, convolution neural networks, recurrent neural networks and deep residual learning. Next, deep real-time video objects detection, tracking and recognition studies are highlighted to illustrate the key trends in terms of cost of computation, number of layers and the accuracy of results. Finally, the paper discusses the challenges of applying DL for real-time video processing and draw some directions for the future of DL algorithms.

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