scholarly journals The Design of Accident Detection and Tracking Systems on Motorcycles

2021 ◽  
pp. 30-36
Author(s):  
Febi Ramadaniati
Keyword(s):  
Sensor Response ◽  
Tracking Systems ◽  
Google Maps ◽  
Detection And Tracking ◽  
Accident Detection ◽  
Formula Method ◽  
Response Area ◽  
Actual Location ◽  
Google Apps

Motorcycles are type of vehicles with the highest accidents percentage every year. The location away from the settlement and the time of the accident is one of the factors that slow down the spread of information about accidents. This study aims to make a tool that can send such information in the form of notifications and crash site points to the victims’ families. Testing was conducted by looking at the sensor response detecting tilt on the motorcycle against the large reading angle. Furthermore, GPS module accuracy testing is conducted in reading the location by comparing GPS module readings with actual location points in Google apps Maps which is then calculated by using the Haversine Formula method up to gsm module capability testing for send SMS notifications in the form of latitude, longitude, and links that can connect to the Google Maps app. Based on the results of the trials and analyses that have been conducted, the slope sensor response area to transmit crash notification is <=50° or >=50°. After that, the GSM module will send notifications and location points detected by the GPS module with an average reading difference of 2.97 meters.

Performance Metrics for Evaluating Object and Human Detection and Tracking Systems

2014 ◽  
Author(s):  
Afzal Godil ◽  
Will Shackleford ◽  
Michael Shneier ◽  
Roger Bostelman ◽  
Tsai Hong
Keyword(s):  
Performance Metrics ◽  
Human Detection ◽  
Tracking Systems ◽  
Detection And Tracking

scholarly journals Sistem Otomatis Pompa Air dan Sabun pada Wastafel Pencuci Tangan

2020 ◽  
Vol 12 (2) ◽  
pp. 53-60
Author(s):  
Andrizal Andrizal ◽  
Yultrisna Yultrisna ◽  
Junaldi Junaldi ◽  
Tuti Anggraini ◽  
Anton Anton
Keyword(s):  
Delay Time ◽  
Minimum Distance ◽  
Sensor Response ◽  
Minimum Time ◽  
Hand Washing ◽  
Maximum Distance ◽  
Vertical Position ◽  
Water Pump ◽  
Response Area

Washing hands is an activity that should be routinely carried out by every individual, especially during the COVID-19 pandemic like today. There are many factors that make someone feel lazy and don't want to wash their hands, one of which is not wanting to be bothered by hand washing activities such as taking soap, turning the faucet and others. Experiments have been carried out in the form of testing the response of the sensor to detect the user's hand based on the distance and the reading angle. Furthermore, the ability to set the delay time for rubbing hands is tested to activate the water tap pump for rinsing. Based on the results of trials and analyzes carried out, the sensor response is obtained at a minimum distance of 4 cm and a maximum distance of 140 cm with a view or response area of 1100 horizontal positions and 850 in the vertical position. The water pump and soap pump are active for 3 seconds when the user's hand is detected by the sensor approaching the end of the water tap. The water tap pump comes back on after being delayed for 23 seconds, and the system is able to operate 100% automatically. Thus the minimum time for users to rub their hands for 20 seconds according to health standard protocols can be met.

scholarly journals Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

2020 ◽  
Vol 13 (6) ◽  
pp. 533-545
Author(s):  
Nuha Abdulghafoor ◽  
◽  
Hadeel Abdullah ◽  
Keyword(s):  
High Accuracy ◽  
Accurate Data ◽  
Tracking Systems ◽  
Video Traffic ◽  
Multiple Objects ◽  
Detection Mechanism ◽  
Data Set ◽  
Detection And Tracking ◽  
Traffic Systems ◽  
Objects Detection

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

scholarly journals Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingfeng Huang ◽  
Yage Huang ◽  
Zhiwei Zhang ◽  
Yujie Zhang ◽  
Weijian Mi ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Detection Algorithm ◽  
Container Terminals ◽  
Multitarget Tracking ◽  
Detection And Tracking ◽  
Prevention System ◽  
Accident Detection ◽  
Vision Detection ◽  
Lifting Equipment ◽  
Traditional Image

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

scholarly journals Semantic Video Mining for Accident Detection

2020 ◽  
Vol 5 (6) ◽  
pp. 670-678
Author(s):  
Rohith G ◽  
Twinkle Roy ◽  
Vishnu Narayan V ◽  
Shery Shaju ◽  
Ann Rija Paul
Keyword(s):  
Object Detection ◽  
Traffic Monitoring ◽  
Machine Learning Techniques ◽  
Efficient System ◽  
The Public ◽  
Detection Algorithms ◽  
Video Mining ◽  
Detection And Tracking ◽  
Learning Techniques ◽  
Accident Detection

This paper depicts the efficient use of CCTV for traffic monitoring and accident detection. The system which is designed has the capability to classify the accident and can give alerts when necessary. Nowadays we have CCTVs on most of the roads, but its capabilities are being underused. There also doesn’t exist an efficient system to detect and classify accidents in real time. So many deaths occur because of undetected accidents. It is difficult to detect accidents in remote places and at night. The proposed system can identify and classify accidents as major and minor. It can automatically alert the authorities if it deals with a major accident. Using this system the response time on accident can be decreased by processing the visuals of CCTV. In this system different image processing and machine learning techniques are used. The dataset for training is extracted from the visuals of already occurred accidents. Accidents mainly occur because of careless driving, alcohol consumption and over speeding. Another main cause of death due to accidents are the delay in reporting accidents since there doesn’t exist any automated systems. Accidents are mainly reported by the public or by traffic authorities. We can save many lives by detecting and reporting the accident quickly. In this system live video is captured from the CCTV’s and it is processed to detect accidents. In this system the YOLOV3 algorithm is used for object detection. Nowadays traffic monitoring has a greater significance. CCTV’s can be used to detect accidents since it is present in most of the roads. It is only used for traffic monitoring. Normally accidents can be classified as two classes major and minor. The proposed system is able to classify the accident as major or minor by object detection and tracking methodologies. Every accident doesn’t need emergency support. Only major accidents must be handled quickly. The proposed system captures the video and undergo object detection algorithms to identify the different objects like vehicles and people. After the detection phase

Sign in / Sign up

Export Citation Format

Share Document