Automatic Traffic Accident Detection Based on the Internet of Things and Support Vector Machine

Liang, Guanzhao

doi:10.14257/ijsh.2015.9.4.10

Cited by 24 publications

(5 citation statements)

References 20 publications

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“…When an accident is detected, a message is sent to the nearest police station and relatives of the victim. Liang et al [58] propose a system that uses support vector machines (SVM) and IoT to efficiently detect accidents. This system is for accident detection and also traffic prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments

Bhatti

Shah

Maple

et al. 2019

Sensors

126

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Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments

Bhatti

Shah

Maple

et al. 2019

Sensors

126

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“…In [6], [7] the use of sensors present inside the vehicle including accelerometers, GSM and GPS modules is made to detect unusual movements and angles of the car to indicate an accident. In [8] the use of sensors like magneto resistive sensors is made outside the vehicle, installed on the roads. In [9], MMA621010EG is a proven special car accident sensor which is integrated XY-axis accelerometer and built-in serial peripheral interface SPI bus.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

Ravindran¹,

Viswanathan²,

Rangaswamy³

2016

ijacsa

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Abstract-In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG) and Gray level co-occurrence matrix (GLCM) features. Since damaged car detection has not been attempted, two datasets of damaged cars -Damaged Cars Dataset-1 (DCD-1) and Damaged Cars Dataset-2 (DCD-2) -was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.

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“…G. Liang [28] presented the automatic detection of accidents with the help of an SVM modified by the algorithm of ant colony (ACA). This allows for traffic accidents to be predicted.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

Khan

Hafeez

Khalid

et al. 2022

Sensors

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In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.

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Automatic Traffic Accident Detection Based on the Internet of Things and Support Vector Machine

Cited by 24 publications

References 20 publications

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments

A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning

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