Efficient City-Wide Multi-Class Multi-Movement Vehicle Counting: A Survey

Abstract

With the advent of accurate deep learning-based object detection methods, it is now possible to employ prevalent city-wide traffic and intersection cameras to derive actionable insights for improving traffic, road infrastructure, and transit. A crucial tool in signal timing planning is capturing accurate movement- and class-specific vehicle counts. To be useful in online intelligent transportation systems, methods designed for this task must not only be accurate in their counting, but should also be efficient. In this paper, we study the multi-class multi-movement vehicle counting problem, overview the state-of-the-art methods designed to solve this problem, and present a series of comprehensive experiments, using traffic footage with O(5) vehicles captured from 20 different vantage points and covering various lighting and weather conditions. Our survey aims to answer the question whether we are ready to leverage traffic cameras for real-time automatic vehicle counting. The results of our analysis show several promising approaches and identify areas where additional improvement is needed.