Spatial Calibration of a Dual PTZ-Fixed Camera System for Tracking Moving Objects in Video

2013 ◽  
Vol 57 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Grzegorz Szwoch ◽  
Piotr Dalka ◽  
Andrzej Czyżewski ◽  
Susan Farnand
Keyword(s):  
Moving Objects ◽  
Camera System ◽  
Spatial Calibration

An Intelligent Security Camera System for Kidnapping Detection

2013 ◽  
Vol 17 (5) ◽  
pp. 746-752 ◽  
Author(s):  
Akira Miyahara ◽  
◽  
Itaru Nagayama
Keyword(s):  
Human Behavior ◽  
Moving Objects ◽  
Video Stream ◽  
Training Data ◽  
Video Streams ◽  
Camera System ◽  
Data Set ◽  
Motion Characteristics ◽  
Key Techniques ◽  
Intelligent Security

In this paper, we propose an automated video surveillance system for kidnapping detection using featurebased characteristics. The localization of moving objects in a video stream and human behavior estimation are key techniques adopted by the proposed system. Some motion characteristics are determined from video streams, and using metrics such as a feature vector, the system automatically classifies the video streams into criminal and non-criminal scenes. The proposed system is called an intelligent security camera. We consider many types of scenarios for the training data set. After constructing the classifier, we use test sequences that are continuous video streams of human behavior consisting of several actions in succession. The experimental results show that the system can effectively detect criminal scenes, such as a kidnapping, by distinguishing human behavior.

Blink-Spot Projection Method for Fast Three-Dimensional Shape Measurement

2015 ◽  
Vol 27 (4) ◽  
pp. 430-443 ◽  
Author(s):  
Jun Chen ◽  
◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
Takeshi Takaki ◽  
...  
Keyword(s):  
Projection Method ◽  
Fast Moving ◽  
High Speed ◽  
Degrees Of Freedom ◽  
Moving Objects ◽  
Three Dimensional ◽  
Frame Rate ◽  
Light Illumination ◽  
Camera System ◽  
3D Shape

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/13.jpg"" width=""300"" /> Blink-spot projection method</div> We present a blink-spot projection method for observing moving three-dimensional (3D) scenes. The proposed method can reduce the synchronization errors of the sequential structured light illumination, which are caused by multiple light patterns projected with different timings when fast-moving objects are observed. In our method, a series of spot array patterns, whose spot sizes change at different timings corresponding to their identification (ID) number, is projected onto scenes to be measured by a high-speed projector. Based on simultaneous and robust frame-to-frame tracking of the projected spots using their ID numbers, the 3D shape of the measuring scene can be obtained without misalignments, even when there are fast movements in the camera view. We implemented our method with a high-frame-rate projector-camera system that can process 512 × 512 pixel images in real-time at 500 fps to track and recognize 16 × 16 spots in the images. Its effectiveness was demonstrated through several 3D shape measurements when the 3D module was mounted on a fast-moving six-degrees-of-freedom manipulator. </span>

scholarly journals A Cost-Efficient Autonomous Air Defense System for National Security

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fazle Rabby Khan ◽  
Md. Muhabullah ◽  
Roksana Islam ◽  
Mohammad Monirujjaman Khan ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Neural Network ◽  
National Security ◽  
Moving Objects ◽  
Defense System ◽  
Camera System ◽  
Human Errors ◽  
Air Defense ◽  
Detection Stage ◽  
Average Loss ◽  
Cost Efficient

In a country, air defense systems are designed to reduce threats efficiently. An air defense system is a fundamental part of any country because it provides national security. This study presents an autonomous air defense system (AADS) development that will automatically detect aerial threats (e.g., drones) and target them without any human intervention. The AADS is implemented using radar, camera, and laser gun. The radar system dynamically emits microwaves and detects moving objects around it. It triggers the camera system if it senses the frequency of any aerial threat. The camera receives the radar’s signal and detects using a neural network algorithm whether it is a threat or not. Neural network algorithms are used for the detection and classification of objects. The laser gun locks its target if the live video feed classifies an object as a more than 75% threat. In the detection stage, an average loss of 0.184961 was achieved using YOLOv3 and 0.155 using the Faster-RCNN. This system will ensure that no human errors are made while detecting threats in a region and improve national safety.

3D Depth Computation of Moving Objects Using a CNN with an Orthogonal Stereo Fisheye Camera System

2019 ◽  
Vol 8 (5) ◽  
pp. 358-366
Author(s):  
Seung Hyun Lee ◽  
Tae Young Han ◽  
Min Kyu Lee ◽  
Kang Il Lee ◽  
Byung Cheol Song
Keyword(s):  
Moving Objects ◽  
Camera System ◽  
Fisheye Camera

scholarly journals Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique

2021 ◽  
Vol 11 (12) ◽  
pp. 5619
Author(s):  
Chieh-Min Liu ◽  
Jyh-Ching Juang
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Moving Objects ◽  
Traffic Flows ◽  
Camera System ◽  
Control Center ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Learning Technique ◽  
And Control

This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.

scholarly journals A Camera System for Picking Moving Objects

1998 ◽  
Vol 31 (28) ◽  
pp. 79-84
Author(s):  
Naoki Amano ◽  
Hiroshi Hashimoto ◽  
Minoru Higashiguchi ◽  
Yukio Kimura
Keyword(s):  
Moving Objects ◽  
Camera System
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