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

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

This dissertation develops a novel system for object recognition in videos. The input of the system is a set of unconstrained videos containing a known set of objects. The output is the locations and categories for each object in each frame across all videos. Initially, a shot boundary detection alg...

Full description

Bibliographic Details
Main Author:	Peng, Liang
Format:	Others
Published:	DigitalCommons@USU 2017
Subjects:	Object Detection Object Recognition Hierarchical Temporal Deep Neural Networks Computer Sciences
Online Access:	https://digitalcommons.usu.edu/etd/6531 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=7703&context=etd

Similar Items

Object recognition with hierarchical discriminant saliency networks
by: Sunhyoung eHan, et al.
Published: (2014-09-01)

Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks
by: Xingyu Liu, et al.
Published: (2020-12-01)

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection
by: Baojun Zhao, et al.
Published: (2018-03-01)

State-of-the-Art Model for Music Object Recognition with Deep Learning
by: Zhiqing Huang, et al.
Published: (2019-06-01)

Deep Learning for Detection of Object-Based Forgery in Advanced Video
by: Ye Yao, et al.
Published: (2017-12-01)

Towards the Design of Neural Network Framework for Object Recognition and Target Region Refining for Smart Transportation Systems
by: Zhao, Yiheng
Published: (2018)

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex
by: Xiaoxuan Jia, et al.
Published: (2021-06-01)

Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study
by: Leonard Elia van Dyck, et al.
Published: (2021-10-01)

A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving
by: Yanfen Li, et al.
Published: (2020-01-01)

Fast object detection based on binary deep convolution neural networks
by: Siyang Sun, et al.
Published: (2018-12-01)

Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells
by: Marcus Lewis, et al.
Published: (2019-04-01)

Multi-Object Grasping Detection With Hierarchical Feature Fusion
by: Guangbin Wu, et al.
Published: (2019-01-01)

Real-Time Moving Object Detection in High-Resolution Video Sensing
by: Haidi Zhu, et al.
Published: (2020-06-01)

Hierarchical Image Object Search Based on Deep Reinforcement Learning
by: Wei Zhang, et al.
Published: (2020-10-01)

MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video
by: Hanyu Wang, et al.
Published: (2018-01-01)

Object Recognition in Aerial Images Using Convolutional Neural Networks
by: Matija Radovic, et al.
Published: (2017-06-01)

Video Object Detection Using Event-Aware Convolutional Lstm and Object Relation Networks
by: Chen Zhang, et al.
Published: (2021-08-01)

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review
by: Lubna Aziz, et al.
Published: (2020-01-01)

Deformation-specific and deformation-invariant visual object recognition: pose vs identity recognition of people and deforming objects
by: Tristan J Webb, et al.
Published: (2014-04-01)

Neural representations that support invariant object recognition
by: Robbe L. T Goris, et al.
Published: (2009-02-01)

Hierarchical Open-Set Object Detection in Unseen Data
by: Yeong Hyeon Kim, et al.
Published: (2019-10-01)

Foreign object debris material recognition based on convolutional neural networks
by: Haoyu Xu, et al.
Published: (2018-04-01)

Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition
by: Sara Tehsin, et al.
Published: (2017-01-01)

Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features
by: DONG Zhipeng, et al.
Published: (2019-10-01)

A Review of Video Object Detection: Datasets, Metrics and Methods
by: Haidi Zhu, et al.
Published: (2020-11-01)

Learning of invariant object recognition in hierarchical neural networks using temporal continuity
by: Markus Lessmann
Published: (2015-12-01)

Object Recognition with Progressive Refinement for Collaborative Robots Task Allocation
by: Wu, Wenbo
Published: (2020)

RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory
by: Hui Zeng, et al.
Published: (2019-01-01)

A Baseline for General Music Object Detection with Deep Learning
by: Alexander Pacha, et al.
Published: (2018-08-01)

A Manufacturing-Oriented Intelligent Vision System Based on Deep Neural Network for Object Recognition and 6D Pose Estimation
by: Guoyuan Liang, et al.
Published: (2021-01-01)

Prior knowledge-based deep learning method for indoor object recognition and application
by: Xintao Ding, et al.
Published: (2018-01-01)

Evaluation of Multiple Object Tracking in Surveillance Video
by: Nyström, Axel
Published: (2019)

AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System
by: Jingyeom Kim, et al.
Published: (2021-06-01)

Survey of Video Object Detection Based on Deep Learning
by: WANG Dicong, BAI Chenshuai, WU Kaijun
Published: (2021-09-01)

Multi-View Object Detection Based on Deep Learning
by: Cong Tang, et al.
Published: (2018-08-01)

Book spine recognition with the use of deep neural networks
by: M.O. Kalinina, et al.
Published: (2020-12-01)

Object Recognition in Very Low Resolution Images Using Deep Collaborative Learning
by: Jeongin Seo, et al.
Published: (2019-01-01)

DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition
by: Yuhuang Hu, et al.
Published: (2016-08-01)

Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images
by: Su-Jin Shin, et al.
Published: (2020-08-01)

A probabilistic integrated object recognition and tracking framework for video sequences
by: Amezquita Gómez, Nicolás
Published: (2009)