Detection and Tracking Multiple Pedestrians from a Moving Camera

2005 ◽  
pp. 527-534 ◽  
Author(s):  
Jong Seok Lim ◽  
Wook Hyun Kim
Keyword(s):  
Moving Camera ◽  
Detection And Tracking

Moving object detection and tracking from video captured by moving camera

2015 ◽  
Vol 30 ◽  
pp. 164-180 ◽  
Author(s):  
Wu-Chih Hu ◽  
Chao-Ho Chen ◽  
Tsong-Yi Chen ◽  
Deng-Yuan Huang ◽  
Zong-Che Wu
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Moving Camera ◽  
Object Detection And Tracking ◽  
Detection And Tracking

ARTOT: Autonomous real-time object detection and tracking by a moving camera

2012 ◽  
Author(s):  
Plamen Angelov ◽  
Chirag Gude ◽  
Pouria Sadeghi-Tehran ◽  
Tsvetan Ivanov
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Moving Camera ◽  
Object Detection And Tracking ◽  
Detection And Tracking

scholarly journals Autonomous detection and tracking under illumination changes, occlusions and moving camera

2015 ◽  
Vol 117 ◽  
pp. 343-354 ◽  
Author(s):  
Harish Bhaskar ◽  
Kartik Dwivedi ◽  
Debi Prosad Dogra ◽  
Mohammed Al-Mualla ◽  
Lyudmila Mihaylova
Keyword(s):  
Moving Camera ◽  
Detection And Tracking ◽  
Illumination Changes ◽  
Autonomous Detection

Detection and tracking of fast moving camera based on the PTZ camera

2016 ◽  
Vol 31 (10) ◽  
pp. 998-1005
Author(s):  
常运 CHANG Yun ◽  
杜玉红 DU Yu-hong ◽  
焉台郎 YAN Tai-lang ◽  
赵地 ZHAO Di ◽  
李兴 LI Xing
Keyword(s):  
Fast Moving ◽  
Moving Camera ◽  
Ptz Camera ◽  
Detection And Tracking

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.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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