scholarly journals COMPUTER VISION BASED TRAFFIC RULE BREACH DETECTION

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Sunim Acharya ◽  
Sujan Poudel ◽  
Shreeya Dangol ◽  
Saragam Subedi
Keyword(s):  
Computer Vision ◽  
Surveillance System ◽  
Traffic Accidents ◽  
Traffic Monitoring ◽  
Traffic Surveillance ◽  
Law Enforcement Agencies ◽  
Traffic Police ◽  
Video Feed ◽  
Cctv Camera ◽  
Traffic Rule

This paper is about the detection of traffic rule breach via computer vision which takes the feed from the traffic surveillance system, processes the video feed, detects the breach and alerts the traffic police. The number of traffic accidents is on the rise with the increasing number of vehicles. Traffic breach is the biggest cause of accidents. So, to mitigate this problem our system processes the CCTV camera feed in real-time, detects the traffic rule breach events and sends the push notification to the android based application of the traffic police stationed nearby; so, further actions can be taken. As this system detects breach faster than humans, the concerned authoritarian department will be at ease in implementing safe roads accurately. This system acts as an add-on to the current video surveillance system rather than building new infrastructure. Thus, the output of this system can be used not only or safety and security purposes but as well as for analytical purposes with effective traffic monitoring at a lower cost. Hence, this system aids law enforcement agencies in implementing road safety efficiently and effectively ensuring smooth traffic flow.

scholarly journals Smart Video Surveillance System Based on Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Smart Camera ◽  
People Detection ◽  
Camera System ◽  
Edge Node ◽  
New Processing ◽  
Processor Unit

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

scholarly journals Smart Indoor Home Surveillance Monitoring System Using Raspberry Pi

2018 ◽  
Vol 2 (4-2) ◽  
pp. 299 ◽  
Author(s):  
Lee Han Keat ◽  
Chuah Chai Wen
Keyword(s):  
Monitoring System ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Motion Sensor ◽  
Analysis And Design ◽  
Everyday Objects ◽  
Smart Surveillance ◽  
Message Service ◽  
Web Camera ◽  
Video Feed

Internet of Things (IoTs) are internet computing devices which are connected to everyday objects that can receive and transmit data intelligently. IoTs allow human to interact and control everyday objects wirelessly to provide more convenience in their lifestyle. The Raspberry Pi is a small, lightweight and cheap single board computer that can fit on human’s palm. Security plays a big role in a home. People concern about security by preventing any intruders to enter their home. This is to prevent loss of privacy and assets. The closed-circuit television (CCTV) is one of the device used to monitor the secured area for any intruders. The use of traditional CCTV to monitor the secured area have three limitations, which are requiring a huge volume of storage to store all the videos regardless there are intruders or not, does not notify the users immediately when there are motions detected, and users must always check the CCTV recorded videos regularly to identity any intruders. Therefore, a smart surveillance monitoring system is proposed to solve this problem by detecting intruders and capturing image of the intruder. Notifications will also be sent to the user immediately when motions are detected. This smart surveillance monitoring system only store the images of the intruders that triggered the motion sensor, making this system uses significantly less storage space. The proposed Raspberry Pi is connected with a passive infrared (PIR) motion sensor, a webcam and internet connection, the whole device can be configured to carry out the surveillance tasks. The objectives of this project are to design, implement and test the surveillance system using the Raspberry Pi. This proposed surveillance system provides the user with live stream of video feed for the user. Whenever a motion is detected by the PIR motion sensor, the web camera may capture an image of the intruder and alert the users (owners) through Short Message Service (SMS) and email notifications. The methodology used to develop this system is by using the object-oriented analysis and design (OOAD) model.

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