Real Time Multi-Vehicle Tracking and Counting at Intersections from a Fisheye Camera

This paper presents an approach for real-time multi-vehicle tracking and counting under fisheye camera based on simple feature points tracking, grouping and association. Different from traditional cameras, the main challenge under fisheye cameras is that the objects being tracked suffer from severe distortion and perspective effects in even adjacent frames. As a result, the points can be stably matched by a point tracker are much fewer, and the points even lose tracking completely quite occasionally. Firstly, to preserve points discrimination in dynamic grouping, we propose an approach based on motion similarity and neighbor-weighted grafting to transfers motion knowledge between long and short point trajectories. Moreover, to deal with cases such as points losing tracking completely or incorrect points grouping, we also propose a concept of points "identity-appearance" that integrates constrained motion for association between vehicle tracklets and segmented point groups. Our approach also overcomes several common challenges in traffic surveillance such as stopping vehicles, pedestrians and counting of linked (partially occluded) vehicles. Finally, extensive experimental results are provided on challenging fisheye image sequences to demonstrate the robustness and effectiveness of the approach.