scholarly journals Motion Recording for Surveillance Camera

2010 ◽  
Vol 7 (2) ◽  
pp. 1070-1075
Author(s):  
Baghdad Science Journal
Keyword(s):  
Motion Detection ◽  
Vision System ◽  
Sudden Change ◽  
Proof Of Concept ◽  
False Detection ◽  
Surveillance Camera ◽  
Time Motion ◽  
Web Camera ◽  
Made In ◽  
Operational Computer

In this paper we present an operational computer vision system for real-time motion detection and recording that can be used in surveillance system. The system captures a video of a scene and identifies the frames that contains motion and record them in such a way that only the frames that is important to us is recorded and a report is made in the form of a movie is made and can be displayed. All parts that are captured by the camera are recorded to compare both movies. This serves as both a proof-of- concept and a verification of other existing algorithms for motion detection. Motion frames are detected using frame differencing. The results of the experiments with the system indicate the ability to minimize some of the problems false detection and missed detections (like in a sudden change of light in the scene). The software part is written in Matlab language as an M-file and using the Simulink library, the hardware part we used a Pentium 4 computer with a web camera or a laptop integrated camera.

Application of Motion Detection Algorithm in Patient Monitoring System

2013 ◽  
Vol 333-335 ◽  
pp. 646-649
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Hai Yan Chen
Keyword(s):  
Mobile Phone ◽  
Motion Detection ◽  
Background Subtraction ◽  
Detection Algorithm ◽  
Object Motion ◽  
Image Subtraction ◽  
Ip Address ◽  
Surveillance Camera ◽  
Web Camera ◽  
Patient Monitoring System

The paper proposes a smart, reliable and robust algorithm for motion detection, tracking and activity analysis. Background subtraction is considered intelligent algorithms for the same. Mount the web camera focused to the patient. PC should have a unique external Internet IP Address. Android mobile phone should be GPRS enabled. GSM technology is used for sending SMS. It is a client-server technology wherein client captures the images, checks for motion if any, discards the packets until motion is detected. Use background subtraction algorithm to check the motion. The surveillance camera does not move and has a capture of the static background it is facing. It uses image subtraction to determine object motion. It provides more reliable information about moving object, but it is so sensitivity to the dynamic changes such as lighting. Once motion is detected, camera stops monitoring further motion. Instead, it starts capturing the video. Simultaneously, SMS alert is sent to the responsible doctors and also alerting the medical staff with audio speaker in the hospital. Java mail API is used to mail the captured video to the entered e-mail IDs. Once the doctor demands for video, socket is established between the PC and the mobile phone and video (series of images) are streamed to the doctors mobile phone. Save live video of first few seconds at the server end for future use. Activate alert at the remote end.

scholarly journals ADVANCED MOTION DETECTION ALGORITHM FOR PATIENT MONITORING USING CELL PHONE WITH VIDEO DISPLAY

2013 ◽  
pp. 175-177
Author(s):  
K. VISHWANATHA ◽  
MURIGENDRAYYA M. HIREMATH
Keyword(s):  
Mobile Phone ◽  
Motion Detection ◽  
Background Subtraction ◽  
Detection Algorithm ◽  
Object Motion ◽  
Image Subtraction ◽  
Surveillance Camera ◽  
Web Camera ◽  
The Subject ◽  
E Mail

Proposed is a smart, reliable and robust algorithm for motion detection, tracking and activity analysis. Background subtraction is considered intelligent algorithms for the same. We use this to track the motion and monitor the movements of the subject in question. Mount the web camera focused to the patient. PC should have a unique external Internet IPAddress. Android mobile phone should be GPRS enabled. GSM technology is used for sending SMS. It is a client-server technology wherein client captures the images, checks for motion if any, discards the packets until motion is detected. Use background subtraction algorithm to check the motion. The surveillance camera does not move and has a capture of the static background it is facing. It uses image subtraction to determine object motion. It provides more reliable information about moving object, but it is so sensitivity to the dynamic changes such as lighting. Once motion is detected, camera stops monitoring further motion. Instead, it starts capturing the video. Simultaneously, SMS alert is sent to the responsible doctors and also alerting the medical staff with audio speaker in the hospital. Java mail API is used to mail the captured video to the entered e-mail IDs. Once the doctor demands for video, socket is established between the PC and the mobile phone and video (series of images) are streamed to the doctor’s mobile phone. Save live video of first few seconds at the server end for future use. Activate alert at the remote end.

Algorithmic Lateral Inhibition Formal Model for Real-Time Motion Detection

2007 ◽  
pp. 638-645 ◽  
Author(s):  
María T. López ◽  
Antonio Fernández-Caballero ◽  
Miguel A. Fernández ◽  
José Mira ◽  
Ana E. Delgado
Keyword(s):  
Real Time ◽  
Lateral Inhibition ◽  
Motion Detection ◽  
Formal Model ◽  
Time Motion

Real Time Motion Detection for Traffic Analysis Using Computer Vision

2020 ◽  
Vol 10 (2) ◽  
pp. 1-14
Author(s):  
Ashwin Sai C. ◽  
Karthik Srinivas K. ◽  
Allwyn Raja P.
Keyword(s):  
Traffic Management ◽  
Traffic Analysis ◽  
Integrated System ◽  
Lane Changing ◽  
Surveillance Camera ◽  
Integrated Method ◽  
Time Motion ◽  
Violation Detection ◽  
Traffic Violations ◽  
Traffic Management System

Nowadays, as the digital era proliferates, there are a number of traffic violation detection systems built using hardware and software to detect violation of traffic rules. This article proposes an integrated method for traffic analysis by detecting vehicles in the video and tracking their motion for multiple violation detection. The purpose of this integrated system is to provide a method to identify different types of traffic violations and to reduce the number of systems used to record violations. This method receives input from traffic surveillance camera and uses DNN to classify the vehicles to reduce the number of personnel needed to do this manually. The authors have implemented modules which are used to track vehicles and detect violations such as line crossing, lane changing, signal jumping, over-speeding and find illegally parked vehicles. The main purpose of this project is to convert manual traffic analysis into a smart traffic management system.

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