Investigation of hidden parameters influencing the automated object detection in images from the deep seafloor of the HAUSGARTEN observatory

2012 Oceans ◽  
2012 ◽  
Author(s):  
T. Schoening ◽  
M. Bergmann ◽  
T. W. Nattkemper
Keyword(s):  
Object Detection ◽  
Hidden Parameters ◽  
Automated Object Detection

scholarly journals IoT Driven Automated Object Detection Algorithm for Urban Surveillance System in Smart City

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1687-1691
Keyword(s):  
Object Detection ◽  
Road Traffic ◽  
Smart Cities ◽  
Digital Camera ◽  
Detection Algorithm ◽  
License Plate ◽  
Surveillance Systems ◽  
Research Issues ◽  
Automated Object Detection ◽  
Research Challenge

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoTdriven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. The other sensors like flame and ultrasonic sensor are used to monitor nearby objects. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems

Fast Automated Object Detection Using Signature Parsing

1990 ◽  
Author(s):  
Tim Heaton ◽  
Shawn Becker ◽  
Kelley Anderson ◽  
William Barrett
Keyword(s):  
Object Detection ◽  
Automated Object Detection

Astronomical Image Analyzer with Automated Object Detection

Space 2005 ◽  
2005 ◽  
Author(s):  
Sonny Orellana ◽  
Richard Nguyen ◽  
Helen Boussalis ◽  
Charles Liu ◽  
Khosrow Rad ◽  
...  
Keyword(s):  
Object Detection ◽  
Image Analyzer ◽  
Astronomical Image ◽  
Automated Object Detection

scholarly journals Automated Object Detection, Mapping, and Assessment of Roadside Clear Zones Using Lidar Data

2021 ◽  
pp. 036119812110299
Author(s):  
Maged Gouda ◽  
Bruno Arantes de Achilles Mello ◽  
Karim El-Basyouny
Keyword(s):  
Object Detection ◽  
Cost Effective ◽  
Design Guidelines ◽  
Lidar Data ◽  
Road Maintenance ◽  
Rural Highways ◽  
Crash Data ◽  
Vehicle Trajectory ◽  
Automated Object Detection ◽  
Effective Source

This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection and Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar data could provide a more efficient and cost-effective source to extract roadside information. This study proposes a novel voxel-based raycasting approach focused primarily on automating roadside mapping and assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, and a method is proposed to extract pavement edge trajectories. Once pavement edges are extracted, guardrails were identified using a conical frustum emitted from the edge trajectory points. Target points and flexion points are then generated and located on the roadside, and a voxel-based raycasting approach is used to search for roadside obstacles and query their locations. Finally, roadside slopes and embankment heights were mapped at specific intervals, and roadside design guidelines and requirements were automatically checked against the mapping results. Noncompliant locations with substandard conditions were automatically queried. The method was tested on four highway segments in Alberta, Canada. The accuracy of the edge detection reached up to 98.5%. Furthermore, the method proved to be accurate in object detection, being able to detect all obstructions on the roadside in each tested segment. The proposed method can help transportation authorities automatically map and inventory roadside clearance parameters. Moreover, the safety performance of existing road infrastructure can be studied using collected information and crash data to support decision making on road maintenance and upgrades.

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 1263-1284
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

Automated Object Detection and Tracking for Intelligent Visual Surveillance Based on Sensor Network

2011 ◽  
pp. 206-228 ◽  
Author(s):  
Ruth Aguilar-Ponce ◽  
Ashok Kumar ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Magdy Bayoumi ◽  
Mark Radle
Keyword(s):  
Wireless Sensor Network ◽  
Object Detection ◽  
Sensor Network ◽  
Tracking System ◽  
Visual Surveillance ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Automated Object Detection

The aim of this research was to apply an agent approach to wireless sensor network in order to construct a distributed, automated scene surveillance. Wireless sensor network using visual nodes is used as a framework for developing a scene understanding system to perform smart surveillance. Current methods of visual surveillance depend on highly train personnel to detect suspicious activity. However, the attention of most individuals degrades after 20 minutes of evaluating monitor-screens. Therefore current surveillance systems are prompt to failure. An automated object detection and tracking was developed in order to build a reliable visual surveillance system. Object detection is performed by means of a background subtraction technique known as Wronskian change detection. After discovery, a multi-agent tracking system tracks and follows the movement of each detected object. The proposed system provides a tool to improve the reliability and decrease the cost related to the personnel dedicated to inspect the monitor-screens

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