Computer Vision-based Accident Detection in Traffic Surveillance

Ijjina, Earnest Paul; Chand, Dhananjai; Gupta, Savyasachi; Goutham, K

doi:10.1109/icccnt45670.2019.8944469

Cited by 81 publications

(27 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They did not consider the environment of human-driven vehicles and lane-changing behavior in different and unexplored places [20] Maximum likelihood estimation, sensors, GPS, V2V High deployment costs; can handle only a linear function [21] DL, convolution network-CNVPS (GCN-CNVPS), GPS, V2V Assumptions made for GPS errors [22] Fuzzy logic, Dempster-Shafer Theory Communication issues not discussed [23] Collision avoidance algorithm Collisions can be identified and avoided in connected vehicles only [24] CCTV surveillance footage Poor results for small vehicles The accident process may take longer due to heuristics [25] Cellular network, multi-hop ideal sending calculation Probability of failure due to unpredicted behaviors in traffic A1. Cont.…”

Section: Refmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

View full text Add to dashboard Cite

An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

show abstract

Section: Refmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Singh et al [11] extracted deep representation via denoising autoencoders trained over the normal traffic videos, and then identified accidents based on the reconstruction error and the likelihood of the deep representation. Ijjina et al [3] utilized Mask R-CNN for object detection and centroid tracking algorithm, which can obtain speed and trajectory anomalies for further crash online inference. Chong et al [12] used a convolutional LSTM auto-encoder to capture regular visual and motion patterns simultaneously for anomaly detection.…”

Section: Traffic Accident Detectionmentioning

confidence: 99%

Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

Chen¹,

Zhang²,

Xiao³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving. This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively. Using 2D region proposals in an RGB image, this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network (FPS-Net) and feature extraction network (FE-Net). Subsequently, the encoder-decoder network (ED-Net) implements 3D-oriented bounding box (OBB) regression. Meanwhile, the adaptive least square regression (ALSR) method is proposed to split 3D OBB. Finally, the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem (SST). In the experiments of KITTI benchmark, our proposed 3D object detector outperforms other state-of-theart methods. Meanwhile, collision detection algorithm achieves the satisfactory performance of 91.8% accuracy on our SHTA dataset.

show abstract

“…Attention based works are demonstrated by [23] which looks for the relevant data points to predict the traffic incidents. Computer vision based [24] techniques are also popular and they exploits the use of real time camera even LiDAR sensor. Use of deep models like autoencoder [25], DNN [26], LSTM [27] etc.…”

Section: A Literature On Accident Predictionmentioning

confidence: 99%

ARIS: A Real Time Edge Computed Accident Risk Inference System

Ovi

Dey

Roy

et al. 2021

2021 IEEE International Conference on Smart Computing (SMARTCOMP)

View full text Add to dashboard Cite

To deploy an intelligent transport system in urban environment, an effective and real-time accident risk prediction method is required that can help maintain road safety, provide adequate level of medical assistance and transport in case of an emergency. Reducing traffic accidents is an important problem for increasing public safety, so accident analysis and prediction have been a subject of extensive research in recent time. Even if a traffic hazard occurs, a readily deployable structure with an accurate prediction of accident can contribute to better management of rescue resources. But the significant shortcomings of current studies are the use of small-scale datasets with minimal scope, being based on extensive data sets, and not being applicable for real-time purposes. To overcome these challenges, we propose ARIS: a system for real-time traffic accident prediction built on a traffic accident dataset named 'US-Accidents' which covers 49 states of United States, collected from February 2016 to June 2020. Our approach is based on a deep neural network model that utilizes a variety of data characteristics, such as time-sensitive weather data, textual information, and discerning factors. We have tested ARIS against multiple baselines through a comprehensive series of experiments across several major cities of USA, and we have noticed significant improvement during inference especially in detecting accident classes. Additionally, to make our model edge-implementable we have compressed our model using a joint technique of magnitude-based weight pruning and model quantization. We have also demonstrated the inference results along with power consumption profiling after deploying the model on a resource constrained environment that consists of Intel Neural Compute Stick 2 (NCS2) with Raspberry Pi 4B (RPi4). Our investigation and observations indicate major improvements to predict unusual traffic accident event even after model compression and deployment. We have managed to reduce the model size and inference time by ≈ 6x, and ≈ 70 % respectively with insignificant drop in performance. Furthermore, to better understand the importance of each individual type of variables used in our analysis, we have showcased a comprehensive ablation study.

show abstract

Computer Vision-based Accident Detection in Traffic Surveillance

Cited by 81 publications

References 12 publications

Smart Roads for Autonomous Accident Detection and Warnings

Smart Roads for Autonomous Accident Detection and Warnings

Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

ARIS: A Real Time Edge Computed Accident Risk Inference System

Contact Info

Product

Resources

About