Combinatorial Use of Optical Tracker, Gaussian Mixture PHD and Group Tracking for Vehicle Light Tracking

Abstract

We propose a combination of optical tracker, Probability Hypothesis Density (PHD) Filter and group tracking for tracking vehicle head lights and tail lights from an in vehicle, forward facing camera. We propose these systems are advantageous, because they can bridge several frames without outside detections and lead to more stable tracks than just using a traditional tracker, like a Kalman filter, on it's own. Additionally PHD does not need track-data association, but moves association uncertainty into the tracker, where it can be incorporated in covariance and noise calculations. Evaluation is performed with a closed source detector and a private data set. This evaluation proves the stability of the tracks and the trackers ability to bridge large amounts of time without external detections. This makes it a suitable choice for high difficulty situations that lead to the external detector missing light sources.