The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in a given location by gathering live stream data from traffic surveillance cameras and transforming it into frames (at a rate of 11 per minute) using the YOLOv3 algorithm, which is a crucial parameter for performing effective traffic diversion by suggesting alternate routes and avoiding traffic congestion. The predicted density is then projected onto Google Maps for the convenience of local clients. The comparative study?s results demonstrate that our strategy consistently and accurately predicts vehicular density, with an accuracy of more than 90 percent under all conditions. It also shows a significant improvement in both precision and recall, with a 4.08 percent improvement.
Short-term traffic analysis, video streaming, Apache Kafka, image frames, you only look once, locality sensitive hashing
K, LAVANYA; TIWARI, STUTI; ANAND, RAHUL; and HEMANTH, JUDE
"YOLO and LSH-based video stream analytics landscape for short-term traffic density surveillance at road networks,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 31:
6, Article 12.
Available at: https://journals.tubitak.gov.tr/elektrik/vol31/iss6/12