VIRTUe: Video surveillance for rail-road traffic safety at unmanned level crossings; (Incorporating Indian scenario)

Gajbhiye, Pranjali; Naveen, Ch.; Satpute, V. R.

doi:10.1109/tenconspring.2017.8070015

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Šilar and Dobrovolny and Ukai et al used optical flow for railway monitoring, but the optical flow was unable to detect static objects ( 13 , 14 ). Gajbhiye et al proposed a five-frame variance foreground-segmentation model; they only used a single image as the background reference and did not have any background update scheme ( 15 ). Li et al introduced a multi-scale image-processing foreground-segmentation method; they only calculated the error between different frames, so they may hardly have detected static objects ( 16 ).…”

Section: Related Workmentioning

confidence: 99%

Railroad Crossing Surveillance and Foreground Extraction Network: Weakly Supervised Artificial-Intelligence Approach

Tang

Wang

Qian

2023

Transportation Research Record: Journal of the Transportation R

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According to the report to Congress National Strategy to Prevent Trespassing on Railroad Property issued by the Federal Railroad Administration, trespassing is currently the number one cause of all railroad-related deaths. The number of fatalities resulting from trespassing, including both illegally entering and remaining in the railroad right-of-way, is even higher than the number of deaths caused by collisions between vehicles and trains. Accurately and effectively detecting objects at rail crossings is critical for improving railway safety. The convolutional neural network (CNN) is one of the most potent tools in image processing and video surveillance. However, the current CNNs under supervised learning rely on a substantial amount of manually labeled images, which is labor-intensive and time-consuming. This study proposes a weakly supervised learning technique for training foreground-segmentation networks and develops a tailored CNN network, RC-SAFE (railroad crossing surveillance and foreground extraction), to accurately and effectively detect and track both still and moving objects in the railroad crossing area. The weakly supervised learning significantly reduces the load for image labeling. The experimental results on the public CDnet 2014 dataset show that the weakly supervised network outperforms the supervised network and some other state-of-the-art foreground-segmentation methods. Railroad crossing monitoring videos are collected at different railroad crossings to validate the proposed RC-SAFE network. The results confirm the robustness of the proposed RC-SAFE network, and that RC-SAFE can detect any object that does not belong to the railroad crossing, which outperforms object-detection-based models, such as Mask RCNN (a region-based CNN).

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Section: Related Workmentioning

confidence: 99%

Railroad Crossing Surveillance and Foreground Extraction Network: Weakly Supervised Artificial-Intelligence Approach

Tang

Wang

Qian

2023

Transportation Research Record: Journal of the Transportation R

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“…In this work, the author presented brief statistics for the number of papers related to the field of human activity detection, object detection, crowd analysis, and violence detection that has been published each year with respect to different journals. Based on this strategy the author enlightens the motivation behind the subject, challenges [22,23], applications, and shortcomings coming out of these research fields.…”

Section: Related Workmentioning

confidence: 99%

Crowd behavior anomaly detection using correlation of optical flow magnitude

Chakole

Satpute

Cheggoju

2022

J. Phys.: Conf. Ser.

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Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.

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“…Authors in [ 9 ] report a five-frame background subtraction model with variance analysis adapted to the specifics of RLC sites. The model is tested using images of hazardous conditions at RLCs such as pedestrian crossing and stopped vehicles.…”

Section: Related Workmentioning

confidence: 99%

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Pamuła

2021

Sensors

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The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.

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VIRTUe: Video surveillance for rail-road traffic safety at unmanned level crossings; (Incorporating Indian scenario)

Cited by 11 publications

References 6 publications

Railroad Crossing Surveillance and Foreground Extraction Network: Weakly Supervised Artificial-Intelligence Approach

Railroad Crossing Surveillance and Foreground Extraction Network: Weakly Supervised Artificial-Intelligence Approach

Crowd behavior anomaly detection using correlation of optical flow magnitude

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

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