Fast Object Tracking in Ordinary Surroundings : Footprint Downsizing of 1000fps Active Camera System

2004 ◽  
Vol 2004 (0) ◽  
pp. 51
Author(s):  
J Oaki ◽  
R Okada ◽  
T Sonoura ◽  
N Kondoh
Keyword(s):  
Object Tracking ◽  
Camera System ◽  
Active Camera

Active Camera System for Object Tracking and Multi-view Observation

2012 ◽  
pp. 45-85
Author(s):  
Takashi Matsuyama ◽  
Shohei Nobuhara ◽  
Takeshi Takai ◽  
Tony Tung
Keyword(s):  
Object Tracking ◽  
Camera System ◽  
Active Camera

scholarly journals Real-Time Object Tracking and 3D Reconstruction by Active Camera

1999 ◽  
Vol 35 (5) ◽  
pp. 675-683 ◽  
Author(s):  
Koichiro DEGUCHI ◽  
Shingo KAGAMI ◽  
Satoshi SAGA ◽  
Hidekata HONTANI
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Real Time ◽  
Active Camera

Fuzzy-based camera selection for object tracking in a multi-camera system

2008 ◽  
Author(s):  
Kazuyuki Morioka ◽  
Szilveszter Kovacs ◽  
Joo-Ho Lee ◽  
Peter Korondi ◽  
Hideki Hashimoto
Keyword(s):  
Object Tracking ◽  
Camera System ◽  
Camera Selection ◽  
Selection For

scholarly journals Traffic Sign Recognition with Invariance to Lighting in Dual-Focal Active Camera System

2012 ◽  
Vol E95.D (7) ◽  
pp. 1775-1790 ◽  
Author(s):  
Yanlei GU ◽  
Mehrdad PANAHPOUR TEHRANI ◽  
Tomohiro YENDO ◽  
Toshiaki FUJII ◽  
Masayuki TANIMOTO
Keyword(s):  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Camera System ◽  
Sign Recognition ◽  
Active Camera

scholarly journals Kinematic calibration of an active camera system

1991 ◽  
Author(s):  
G S Young ◽  
T H Hong ◽  
M Herman ◽  
J C S Yang
Keyword(s):  
Kinematic Calibration ◽  
Camera System ◽  
Active Camera

scholarly journals Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning

2021 ◽  
Vol 33 (6) ◽  
pp. 1303-1314
Author(s):  
Masato Fujitake ◽  
Makito Inoue ◽  
Takashi Yoshimi ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Object Detection ◽  
Object Tracking ◽  
Tracking System ◽  
Construction Technology ◽  
Camera System ◽  
Automatic Tracking ◽  
Machine Operators ◽  
Surveillance Cameras

This paper describes the development of a robust object tracking system that combines detection methods based on image processing and machine learning for automatic construction machine tracking cameras at unmanned construction sites. In recent years, unmanned construction technology has been developed to prevent secondary disasters from harming workers in hazardous areas. There are surveillance cameras on disaster sites that monitor the environment and movements of construction machines. By watching footage from the surveillance cameras, machine operators can control the construction machines from a safe remote site. However, to control surveillance cameras to follow the target machines, camera operators are also required to work next to machine operators. To improve efficiency, an automatic tracking camera system for construction machines is required. We propose a robust and scalable object tracking system and robust object detection algorithm, and present an accurate and robust tracking system for construction machines by integrating these two methods. Our proposed image-processing algorithm is able to continue tracking for a longer period than previous methods, and the proposed object detection method using machine learning detects machines robustly by focusing on their component parts of the target objects. Evaluations in real-world field scenarios demonstrate that our methods are more accurate and robust than existing off-the-shelf object tracking algorithms while maintaining practical real-time processing performance.

Sign in / Sign up

Export Citation Format

Share Document