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Visual Object Tracking with Deep Neural Networks
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Название: Visual Object Tracking with Deep Neural Networks
Автор: Pier Luigi Mazzeo, Srinivasan Ramakrishnan, Paolo Spagnolo
Издательство: ITExLi
Год: 2019
Страниц: 184
Язык: английский
Формат: pdf (true)
Размер: 35.2 MB

This volume is dedicated to visual object tracking (VOT) and face recognition (FR) and presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using Deep Learning (DL).

Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning.

The bok is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Contents:
1.Deep Siamese Networks toward Robust Visual Tracking
2.Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features
3.Detecting and Counting Small Animal Species Using Drone Imagery by Applying Deep Learning
4.Deep-Facial Feature-Based Person Reidentification for Authentication in Surveillance Applications
5.Object Re-Identification Based on Deep Learning
6.Spatial Domain Representation for Face Recognition
7.Extended Binary Gradient Pattern (eBGP): A Micro- and Macrostructure-Based Binary Gradient Pattern for Face Recognition in Video Surveillance Area
8.Matrix Factorization on Complex Domain for Face Recognition
9.Granular Approach for Recognizing Surgically Altered Face Images Using Keypoint Descriptors and Artificial Neural Network

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