Clustering Local Motion Estimates for Robust and Efficient Object Tracking

摘要

We present a new short-term tracking algorithm called Best Displacement Flow (BDF). This approach is based on the idea of 'Flock of Trackers' with two main contributions. The first contribution is the adoption of an efficient clustering approach to identify what we term the 'Best Displacement' vector, used to update the object's bounding box. This clustering procedure is more robust than the median filter to high percentage of outliers. The second contribution is a procedure that we term 'Consensus-Based Reinitialization' used to reinitialize trackers that have previously been classified as outliers. For this reason we define a new tracker state called 'transition' used to sample new trackers in according to the current inlier trackers.