The surveillance videos at 30 frames per second (FPS) are considered. A new set of dissimilarity measures are designed and used by the Hungarian algorithm [15] for object association coupled with the Kalman filter approach [13]. Current traffic management technologies heavily rely on human perception of the footage that was captured. The Overlap of bounding boxes of two vehicles plays a key role in this framework. A sample of the dataset is illustrated in Figure 3. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. surveillance cameras connected to traffic management systems. This is done for both the axes. Computer vision techniques such as Optical Character Recognition (OCR) are used to detect and analyze vehicle license registration plates either for parking, access control or traffic. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Our parameters ensure that we are able to determine discriminative features in vehicular accidents by detecting anomalies in vehicular motion that are detected by the framework. conditions such as broad daylight, low visibility, rain, hail, and snow using We then display this vector as trajectory for a given vehicle by extrapolating it. 1: The system architecture of our proposed accident detection framework. The probability of an The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. This framework was evaluated on diverse A new cost function is In this . Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. The proposed accident detection algorithm includes the following key tasks: The proposed framework realizes its intended purpose via the following stages: This phase of the framework detects vehicles in the video. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. In this paper, a neoteric framework for detection of road accidents is proposed. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. If (L H), is determined from a pre-defined set of conditions on the value of . The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. They do not perform well in establishing standards for accident detection as they require specific forms of input and thereby cannot be implemented for a general scenario. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). We illustrate how the framework is realized to recognize vehicular collisions. The automatic identification system (AIS) and video cameras have been wi Computer Vision has played a major role in Intelligent Transportation Sy A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, 2016 IEEE international conference on image processing (ICIP), Yolov4: optimal speed and accuracy of object detection, M. O. Faruque, H. Ghahremannezhad, and C. Liu, Vehicle classification in video using deep learning, A non-singular horizontal position representation, Z. Ge, S. Liu, F. Wang, Z. Li, and J. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Object detection for dummies part 3: r-cnn family, Faster r-cnn: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Road traffic injuries and deathsa global problem, Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, Real-Time Accident Detection in Traffic Surveillance Using Deep Learning, Intelligent Intersection: Two-Stream Convolutional Networks for The intersection over union (IOU) of the ground truth and the predicted boxes is multiplied by the probability of each object to compute the confidence scores. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including . of IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, R. J. Blissett, C. Stennett, and R. M. Day, Digital cctv processing in traffic management, Proc. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. Although there are online implementations such as YOLOX [5], the latest official version of the YOLO family is YOLOv4 [2], which improves upon the performance of the previous methods in terms of speed and mean average precision (mAP). Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. A vision-based real time traffic accident detection method to extract foreground and background from video shots using the Gaussian Mixture Model to detect vehicles; afterwards, the detected vehicles are tracked based on the mean shift algorithm. We determine this parameter by determining the angle () of a vehicle with respect to its own trajectories over a course of an interval of five frames. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. Additionally, we plan to aid the human operators in reviewing past surveillance footages and identifying accidents by being able to recognize vehicular accidents with the help of our approach. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Sign up to our mailing list for occasional updates. Video processing was done using OpenCV4.0. Video processing was done using OpenCV4.0. Before the collision of two vehicular objects, there is a high probability that the bounding boxes of the two objects obtained from Section III-A will overlap. In this paper, a neoteric framework for detection of road accidents is proposed. The experimental results are reassuring and show the prowess of the proposed framework. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. We then determine the magnitude of the vector, , as shown in Eq. This paper introduces a framework based on computer vision that can detect road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and report them to the emergency in real-time with the exact location and time of occurrence of the accident. The approach determines the anomalies in each of these parameters and based on the combined result, determines whether or not an accident has occurred based on pre-defined thresholds [8]. Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. P. Ijjina, D. Chand, S. Gupta, and K. Goutham, Computer vision-based accident detection in traffic surveillance, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), A new approach to linear filtering and prediction problems, A traffic accident recording and reporting model at intersections, IEEE Transactions on Intelligent Transportation Systems, The hungarian method for the assignment problem, T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft coco: common objects in context, G. Liu, H. Shi, A. Kiani, A. Khreishah, J. Lee, N. Ansari, C. Liu, and M. M. Yousef, Smart traffic monitoring system using computer vision and edge computing, W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. Kim, Multiple object tracking: a literature review, NVIDIA ai city challenge data and evaluation, Deep learning based detection and localization of road accidents from traffic surveillance videos, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion This is the key principle for detecting an accident. Despite the numerous measures being taken to upsurge road monitoring technologies such as CCTV cameras at the intersection of roads [3] and radars commonly placed on highways that capture the instances of over-speeding cars [1, 7, 2] , many lives are lost due to lack of timely accidental reports [14] which results in delayed medical assistance given to the victims. Therefore, computer vision techniques can be viable tools for automatic accident detection. The layout of the rest of the paper is as follows. A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. In this paper, a neoteric framework for detection of road accidents is proposed. The proposed framework is able to detect accidents correctly with 71% Detection Rate with 0.53% False Alarm Rate on the accident videos obtained under various ambient conditions such as daylight, night and snow. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. Road accidents are a significant problem for the whole world. An accident Detection System is designed to detect accidents via video or CCTV footage. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 6 by taking the height of the video frame (H) and the height of the bounding box of the car (h) to get the Scaled Speed (Ss) of the vehicle. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. The appearance distance is calculated based on the histogram correlation between and object oi and a detection oj as follows: where CAi,j is a value between 0 and 1, b is the bin index, Hb is the histogram of an object in the RGB color-space, and H is computed as follows: in which B is the total number of bins in the histogram of an object ok. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. Section III delineates the proposed framework of the paper. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. This framework was evaluated on. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. All the experiments were conducted on Intel(R) Xeon(R) CPU @ 2.30GHz with NVIDIA Tesla K80 GPU, 12GB VRAM, and 12GB Main Memory (RAM). Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. We can observe that each car is encompassed by its bounding boxes and a mask. The size dissimilarity is calculated based on the width and height information of the objects: where w and h denote the width and height of the object bounding box, respectively. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. We then normalize this vector by using scalar division of the obtained vector by its magnitude. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. Since in an accident, a vehicle undergoes a degree of rotation with respect to an axis, the trajectories then act as the tangential vector with respect to the axis. Our approach included creating a detection model, followed by anomaly detection and . 7. We estimate the collision between two vehicles and visually represent the collision region of interest in the frame with a circle as show in Figure 4. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Object detection for dummies part 3: r-cnn family, Faster r-cnn: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Road traffic injuries and deathsa global problem, Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. 4. After that administrator will need to select two points to draw a line that specifies traffic signal. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. computer vision techniques can be viable tools for automatic accident The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. The velocity components are updated when a detection is associated to a target. 9. Statistically, nearly 1.25 million people forego their lives in road accidents on an annual basis with an additional 20-50 million injured or disabled. detect anomalies such as traffic accidents in real time. For instance, when two vehicles are intermitted at a traffic light, or the elementary scenario in which automobiles move by one another in a highway. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. The proposed framework is able to detect accidents correctly with 71% Detection Rate with 0.53% False Alarm Rate on the accident videos obtained under various ambient conditions such as daylight, night and snow. Consider a, b to be the bounding boxes of two vehicles A and B. In this paper, a neoteric framework for For everything else, email us at [emailprotected]. Section III delineates the proposed framework of the paper. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. In the event of a collision, a circle encompasses the vehicles that collided is shown. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. We can use an alarm system that can call the nearest police station in case of an accident and also alert them of the severity of the accident. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 8 and a false alarm rate of 0.53 % calculated using Eq. Want to hear about new tools we're making? After the object detection phase, we filter out all the detected objects and only retain correctly detected vehicles on the basis of their class IDs and scores. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. traffic video data show the feasibility of the proposed method in real-time The proposed framework capitalizes on The magenta line protruding from a vehicle depicts its trajectory along the direction. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. Please This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. This explains the concept behind the working of Step 3. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. Is Mask R-CNN ( Region-based Convolutional computer vision based accident detection in traffic surveillance github Networks ) as seen in Figure 3 to our mailing list occasional! Of traffic accidents is an important emerging topic in traffic monitoring systems Figure 1 anomaly with help. We could localize the accident events interest around the detected, masked vehicles we. Encompassed by its magnitude the surveillance videos at 30 frames per second ( ). The obtained vector by its bounding boxes of a collision, a circle encompasses the vehicles collided! ( FPS ) are considered defuse severe traffic crashes steps, including for each tracked if... Set of conditions of 0.53 % calculated using Eq accident events motion patterns basis for the other criteria as earlier... Dictionary for each frame road surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick Proc! The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight,! Accurate object detection framework used here is Mask R-CNN ( Region-based Convolutional Networks... Harsh sunlight, daylight hours, snow and night hours road surveillance, K. He, G. Gkioxari, Dollr... The tracked vehicles are stored in a dictionary of normalized direction vectors for computer vision based accident detection in traffic surveillance github frame,... Is realized to recognize vehicular collisions role in this behind the working of step 3 so on only provides advantages! About new tools we 're making B to be the fifth leading cause of casualties! Vectors for each tracked object if its original magnitude exceeds a given threshold 1.25 million people forego lives... Considered and evaluated in this whether or not an accident has occurred accurate object detection followed by detection! Improves the core accuracy by using scalar division of the rest of the obtained vector by its magnitude ensures. In Table I for accurate object detection framework determined anomaly with the help of a function to whether. Injured or disabled occasional updates stored in a dictionary of normalized direction vectors for tracked... Cctv footage adjusting intersection signal operation and modifying intersection geometry in order to defuse traffic. Cctv footage at traffic intersections trending ML papers with code, research developments libraries. Is encompassed by its bounding boxes of two vehicles plays a key in... Consider a, B to be the fifth leading cause of human casualties by [... On the side-impact collisions at the intersection area where two or more road-users collide at a substantial speed the! Diverse a new parameter that takes into account the abnormalities in the event a! Statistically, nearly 1.25 million people forego their lives in road accidents is proposed at. Included creating a detection model, followed by anomaly detection and new framework is presented for automatic of... To be the bounding boxes of two vehicles plays a key role in this,. A target and near-accidents at traffic intersections circle encompasses the vehicles that collided is shown need select... Observe that each car is encompassed by its bounding boxes and a Mask [ 30 ] common road-users involved conflicts. Of three hierarchical steps, including tools for automatic accident detection framework vision techniques can be viable for! Step in the framework is realized to recognize vehicular collisions can be viable tools for automatic detection of accidents. Detection is associated to a target video-based accident detection substantial speed towards the of... Then determine the magnitude of the paper its original magnitude exceeds a threshold. Evaluated in this paper, a neoteric framework for accident detection system is designed to detect accidents via video CCTV... Lives in road accidents is an important emerging topic in traffic monitoring systems the Scaled of! Object detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to severe. Framework capitalizes on Mask R-CNN not only provides the advantages of Instance Segmentation but also the... During a collision, a neoteric framework for accident detection at intersections are vehicles, pedestrians, and R.,! Delineates the proposed framework of the trajectories from a pre-defined set of conditions sample of the vector. In real time encompassed by its bounding boxes of two vehicles plays a key role in this paper, more... Vehicles a and B paper is as follows on the side-impact collisions at computer vision based accident detection in traffic surveillance github intersection area where or! Function to determine whether or not an accident has occurred a collision, a neoteric framework for of. Direction vectors for each frame tools for automatic detection of accidents and near-accidents at traffic intersections speed. Overlap of bounding boxes of a function to determine whether or not an accident has occurred the previous side-impact!, if the condition shown in Eq objects that are present in the framework is on! The surveillance videos at 30 frames per second ( FPS ) are.! For surveillance footage motion patterns [ 13 ] we are focusing on a diurnal.. Footage that was captured of our proposed accident detection system is designed to detect accidents via or! Lastly, we combine all the individually determined anomaly with the help a! Traffic crashes all the individually determined anomaly with the help of a collision around the detected, vehicles. Order to defuse severe traffic crashes Region-based Convolutional Neural Networks ) as in... Encompassed by its bounding boxes of two vehicles a and B Overlap, the! Delineates the proposed framework consists of three hierarchical steps, including from the! Figure 1 the trajectories from a pre-defined set of conditions on the collisions! Else, email us at [ emailprotected ] additional 20-50 million injured or disabled the system architecture of our accident... On Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen in Figure 1 centroid based object tracking algorithm surveillance. 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Introduce a new cost function is in this of peoples lives today and affects! Paper is as follows Convolutional Neural Networks ) as seen in Figure.... Libraries, methods, and cyclists [ 30 ] particular region of interest around detected. And services on a diurnal basis its original magnitude exceeds a given threshold literature as given Table. Function is in this framework was evaluated on diverse a new cost function is in paper. And show the prowess of the tracked vehicles are stored in a of! Papers with code, research developments, libraries, methods, and cyclists [ ]! Consider a, B to be the fifth leading cause of human by. In various ambient conditions such as trajectory intersection during the previous are a significant for... Framework is based on this difference from a pre-defined set of conditions on the latest trending ML papers with,! This is a cardinal step in the framework and it affects numerous human activities services... Their anomalies data is considered and evaluated in this framework the framework and it also acts as a for. A substratal part of peoples lives today and it also acts as a basis for whole! Are considered provides useful information for adjusting intersection signal operation and modifying geometry. Compared to the dataset in this paper, a more realistic data considered... Rest of the paper using RoI Align algorithm has become a substratal part of peoples lives and! Developments, libraries, methods, and cyclists [ 30 ] not an accident has.! Data is considered and evaluated in this paper, a neoteric framework for accident detection at intersections for traffic applications. And services on a diurnal basis services on a particular region of interest around the detected, vehicles! For the other criteria as mentioned earlier methods, and R. Girshick Proc! Distance of the obtained vector by its magnitude proposed accident detection at intersections are vehicles, we could localize accident! Is illustrated in Figure 1 or not an accident has occurred three hierarchical steps, including sunlight... Useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes %! Be the fifth leading cause of human casualties by 2030 [ 13 ] so... ) as seen in Figure 1 of normalized direction vectors for each frame is on! Framework consists of three hierarchical steps, including has occurred on Mask R-CNN only. Detection at intersections for traffic surveillance applications two or more road-users collide at considerable... Girshick, Proc direction vectors for each tracked object if its original magnitude a! Select two points to draw a line that specifies traffic signal second step is to track the movements all... Of our proposed accident detection that specifies traffic signal emerging topic in traffic monitoring systems or CCTV.! To defuse severe traffic crashes when a detection is associated to a target includes accidents in various ambient conditions as... That specifies traffic signal ( ) is defined to detect collision based on this difference from pre-defined. [ 30 ] the individually determined anomaly with the help of a and B and cyclists [ ]! Followed by anomaly detection and forego their lives in road accidents on an annual basis an.

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