Real-time estimation of travel speed using urban traffic information system and CCTV

Ki, Yong-Kul; Choi, Jinwook; Joun, Ho-Jin; Ahn, Gye-Hyeong; Cho, Kyucheol

doi:10.1109/iwssip.2017.7965582

Cited by 8 publications

(1 citation statement)

References 7 publications

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“…If the vehicle has not been distinguished in the frame for five seconds, the most recently accessible past centroid is taken. The Gross Speed (S g ) is resolved from centroid difference found out over the Interval of five frames using (5).…”

Section: F Vehicle Speed and Change In Acceleration Determinationmentioning

confidence: 99%

Computer Vision based Accident Detection for Autonomous Vehicles

Chand

Gupta

Kavati

2020

2020 IEEE 17th India Council International Conference (INDICON)

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Numerous Deep Learning and sensor-based models have been developed to detect potential accidents with an autonomous vehicle. However, a self-driving car needs to be able to detect accidents between other vehicles in its path and take appropriate actions such as to slow down or stop and inform the concerned authorities. In this paper, we propose a novel support system for self-driving cars that detects vehicular accidents through a dashboard camera. The system leverages the Mask R-CNN framework for vehicle detection and a centroid tracking algorithm to track the detected vehicle. Additionally, the framework calculates various parameters such as speed, acceleration, and trajectory to determine whether an accident has occurred between any of the tracked vehicles. The framework has been tested on a custom dataset of dashcam footage and achieves a high accident detection rate while maintaining a low false alarm rate.

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Section: F Vehicle Speed and Change In Acceleration Determinationmentioning

confidence: 99%