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Object Recognition in Videos Utilizi...

Peng, Liang.

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
作者:	Peng, Liang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2017
面頁冊數:	111 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
附註:	Adviser: Xiaojun Qi.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10619161
ISBN:	9780355161427

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
Peng, Liang.

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 111 p.

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Utah State University, 2017.

As the growth of mobile devices and social networks has been faster than ever, online image and video content have become truly ubiquitous today. Understanding of these images and videos, called vision, is one of the most primary ways for the human being to perceive the world. Computer vision, which refers to the study of enabling machines to see and understand the visual world, is fundamental in advancing Artificial Intelligence.

ISBN: 9780355161427Subjects--Topical Terms:

199325
Computer science.

Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.
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520 $a Object recognition, which is defined as the task of locating and recognizing object categories in images and videos, is a major research field in computer vision. Recent research in object recognition has achieved some significant improvement utilizing larger labeled data (e.g., ImageNet) and deep architecture of neural network algorithms (e.g.,Convolution Neutral Network, Restricted Boltzmann Machine, etc.). However, object recognition research using deep architectures has been mainly focused on images. Little has been done in videos, one of the fastest growing types of multimedia content. Video understanding, especially large-scale object detection in video, has applications in brand awareness, autonomous cars, augmented reality, etc.
520 $a The research presented in this dissertation proposes and demonstrates a novel system that automatically recognizes objects in videos by incorporating tracking, object detection and classification using deep neural networks. By utilizing temporal and spatial information, the proposed approach achieved the better object recognition performance than the prior state-of-the-art methods in terms of the average precision.
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