Abstract
This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data.
Highlights
Urbanization and increased building density of cities are essential features of modern society
We have proposed and implemented a novel traffic flow estimation system, which is based on the recent advances in vehicle detection and tracking tasks
This paper aims to develop a system for traffic flow estimation, i.e. for counting and classifying vehicles by their movement directions
Summary
Urbanization and increased building density of cities are essential features of modern society. Does such a way of life bring economic benefits, but it poses a new set of problems for city authorities. One of these problems is efficient traffic management and analysis. High population density leads to the tremendous number of personal cars, an increased number of freight vehicles for transportation of commodities and goods, tight pedestrian traffic. Transportation tasks can no longer be addressed by sub-optimal heuristics, based on the small amount of the manually gathered statistics. Forecast and assess their consequences, authorities require an automated system for analyzing traffic flow throughout the city
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