Detection, Tracking, and Speed Estimation of Vehicles: A Homography-based Approach

In this research we present a parsimonious yet effective method to detect, track, and estimate the speed of multiple vehicles using a single camera. This research aims to determine the efficacy of homography-based speed estimations derived from details extracted from objects of interest. At first, a neural network trained to detect vehicles outputs bounding boxes. The output of the neural network serves as an input to a multi-object tracking algorithm which tracks the detected vehicles while, at the same time, their speed is estimated through a homography-based approach. This algorithm makes no assumptions about the camera, the distance to the objects, or the direction of motion of vehicles with respect to the camera. This method proves to be accurate and efficient with minimal assumptions. In particular, only the mean dimensions of a passenger vehicle are assumed to be known and, using the homography matrix derived from the corners of a vehicle, the speed of any vehicle in the frame irrespective of its motion direction and regardless of its size is able to be estimated. In addition, only a single point from each tracked vehicle is needed to infer its speed, avoiding repeatedly computing the homography matrix for each and every vehicle, thus reducing the time and computational complexity of the algorithm. We have tested our algorithm on a series of known datasets, the results from which validate the approach.