Accident Detection Using Convolutional Neural Networks

Ghosh, Sreyan; Sunny, Sherwin Joseph; Roney, Rohan

doi:10.1109/icondsc.2019.8816881

Cited by 67 publications

(19 citation statements)

References 3 publications

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“…On the other hand, Ghosh et al (7) pointed out that traffic accidents are one of the important causes of death in India. Indeed, more than 80% of the time, the death is not caused by the accident itself but by the lack of timely help for the accident victim.…”

Section: Research On Freeway Traffic Recognitionmentioning

confidence: 99%

“…On very busy, chaotic, highspeed highways, the main cause of accidents can be prolonged periods of unattended traffic. Therefore, the main purpose of their work (7) was to create a system that will detect accidents based on real-time video footage from CCTV installed on the highway. The idea is to obtain each frame of the video and execute the video through a deep learning convolutional neural network (CNN) model that has been trained to classify frames of video images as accidental or non-accidental.…”

Section: Research On Freeway Traffic Recognitionmentioning

confidence: 99%

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Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

Pan¹,

Wang²

2021

Sensors and Materials

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This study focuses on object detection in computer vision research. The object detection process often encounters many uncertainties, such as the uncertainty of the number of objects in the image, the different conditions of the objects including their appearance, the current driving speed, the obstruction between vehicles, sunlight in the daytime, the lack of light at night, the irreversible factors related to the CCTV lens, and other factors, which make object detection and image preprocessing difficult. Taiwan's freeways are all equipped with CCTV to monitor realtime road conditions, and all CCTV images are available to the public via the internet. However, in freeway segments and tunnels, and even on traffic-prone roads, traffic jams and accidents are only judged by "human power." Therefore, in this study, we use existing CCTV streaming video as a vehicle sensor data source and the You Only Look Once (YOLO) algorithm to perform object detection as well to tune adjustable parameters to achieve the desired results. From the preliminary results of this study, the current model based on the YOLOv3 algorithm and the Common Objects in Context (COCO) image dataset has an accuracy of 44% during the daytime and 41% during the nighttime for CCTV cameras installed outdoors. In the future, we will analyze larger amounts of CCTV video streaming data to detect whether a road is congested and even detect the occurrence of traffic accidents.

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Section: Research On Freeway Traffic Recognitionmentioning

confidence: 99%

Section: Research On Freeway Traffic Recognitionmentioning

confidence: 99%

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

Pan¹,

Wang²

2021

Sensors and Materials

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“…In [18] a system is proposed that can detect an accident from the video footage obtained from the CCTV cameras installed on highways. Each frame of video is given as an input to the Convolutional Neural Network (CNN) model, which is able to distinguish frames of video into accident and non-accident.…”

Section: C: Accident Detection Using Artificial Neural Networkmentioning

confidence: 99%

“…Different algorithms such as k-means clustering, SVM under reinforcement learning by fetching real time data like velocity which is obtained by means of vibration sensors installed in vehicle and distance which is obtained by means of ultrasonic sensors [17]. Accidents can also be detected by pre-trained surveillance cameras installed on the highways [18], as illustrated in Figure 1. Another machine learning approach uses fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

et al. 2020

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With population growth, the demand for vehicles has increased tremendously, which has created an alarming situation in terms of traffic hazards and road accidents. The road accidents percentage is growing exponentially and so are the fatalities caused due to accidents. However, the primary cause of the increased rate of fatalities is due to the delay in emergency services. Many lives could be saved with efficient rescue services. The delay happens due to traffic congestion or unstable communication to the medical units. The implementation of automatic road accident detection systems to provide timely aid is crucial. Many solutions have been proposed in the literature for automatic accident detection .The techniques include crash prediction using smartphones, vehicular ad-hoc networks, GPS/GSM based systems, and various machine learning techniques. With such high rates of deaths associated with road accidents, road safety is the most critical sector that demands significant exploration. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives.

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“…At the time of compile, tasks are distributed to a various number of processors in Static load balancing. At run time, different types of tasks are together collected in dynamic algorithms [1]. Structure of electronics and dynamics of molecular are intermittent problems that are solved using parallel techniques of genetic algorithms that support refinement mesh [2].…”

Section: Types Of Load Balancing Algorithmsmentioning

confidence: 99%

Load Balancing Analyzer: A Recommendation System using Machine Learning

Tata¹

2020

IJETER

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The Parallel and distributed systems focus on the concept of load balancing. A tangle is split into a hard and fast number of processors that are to be executed in parallel. However, there may be a state that some processors will complete their tasks before other processes and reach idle state as the work is unevenly divided or perhaps some processors complete before the others. Ideally, we like all the processors to have the minimum wait time. Achieving the above goal by spreading the tasks is eventually named as load balancing. In the paper, we stress on clustering techniques. The basis of the join-idle-queue algorithm is seen by using clustering. The technique of load balancing uses Support Vector Machine (SVM) and clustering techniques (K-means, Hierarchical). A comparative study of the above techniques is done utilizing load balancing.

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Accident Detection Using Convolutional Neural Networks

Cited by 67 publications

References 3 publications

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

Identifying Vehicles Dynamically on Freeway CCTV Images through the YOLO Deep Learning Model

A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles

Load Balancing Analyzer: A Recommendation System using Machine Learning

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