Research of Vehicle Video Analysis System Based on SVM

2016 ◽  
Author(s):  
Yanqiu Wang ◽  
Wei Zhang ◽  
Bin Yang ◽  
Caixia Liu ◽  
Xiaofei Yan
Keyword(s):  
Video Analysis ◽  
Analysis System

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Time course of changes in capillarization in hypertrophied rat plantaris muscle

1998 ◽  
Vol 84 (3) ◽  
pp. 902-907 ◽  
Author(s):  
Michael J. Plyley ◽  
Barbara J. Olmstead ◽  
Earl G. Noble
Keyword(s):  
Muscle Fiber ◽  
Video Analysis ◽  
Time Course ◽  
Fiber Type ◽  
Common Factor ◽  
Sectional Area ◽  
Plantaris Muscle ◽  
Cross Sectional ◽  
Tightly Coupled ◽  
Analysis System

The time course of angiogenesis during hypertrophy of the rat plantaris muscle was studied by using a unilateral, synergistic ablation model. Animals ( n = 6/group) were euthanized 2, 5, 7, 15, 21, and 30 days postmyectomy. Sections from both the hypertrophied and contralateral muscles were simultaneously stained for capillaries and muscle fiber type. Mean fiber cross-sectional area (FA) and various indexes of capillarity were determined by using a video analysis system. The capillary supply to individual fibers, assessed as the FA supplied per capillary contact, remained unchanged until day 21 (compared with day 2) and exhibited a significant increase at day 30. Analysis of the time course of capillary development on the basis of the number of capillary contacts per fiber, and of hypertrophy on the basis of FA, yielded half-lives of 10.1 and 11.2 days, respectively. It was concluded that angiogenesis during muscle overload is tightly coupled to the changes in FA, which could suggest that the two processes are initiated and/or driven by some common factor(s).

scholarly journals Automated video analysis system reveals distinct diurnal behaviors in C57BL/6 and C3H/HeN mice

2013 ◽  
Vol 243 ◽  
pp. 306-312 ◽  
Author(s):  
E.B. Adamah-Biassi ◽  
I. Stepien ◽  
R.L. Hudson ◽  
M.L. Dubocovich
Keyword(s):  
Video Analysis ◽  
Analysis System ◽  
Automated Video Analysis

The usage of video analysis system for detection of immobility in the tail suspension test in mice

2006 ◽  
Vol 85 (2) ◽  
pp. 332-338 ◽  
Author(s):  
G JUSZCZAK ◽  
A SLIWA ◽  
P WOLAK ◽  
A TYMOSIAKZIELINSKA ◽  
P LISOWSKI ◽  
...  
Keyword(s):  
Video Analysis ◽  
Tail Suspension Test ◽  
Tail Suspension ◽  
Analysis System ◽  
Suspension Test

A cloud-based large-scale distributed video analysis system

2016 ◽  
Author(s):  
Yongzhe Wang ◽  
Wei-Ta Chen ◽  
Huahui Wu ◽  
Anil Kokaram ◽  
Jaron Schaeffer
Keyword(s):  
Video Analysis ◽  
Large Scale ◽  
Analysis System
Sign in / Sign up

Export Citation Format

Share Document