  The paper evaluates the error performance of three random finite set based
multi-object trackers in the context of pedestrian video tracking. The
evaluation is carried out using a publicly available video dataset of 4500
frames (town centre street) for which the ground truth is available. The input
to all pedestrian tracking algorithms is an identical set of head and body
detections, obtained using the Histogram of Oriented Gradients (HOG) detector.
The tracking error is measured using the recently proposed OSPA metric for
tracks, adopted as the only known mathematically rigorous metric for measuring
the distance between two sets of tracks. A comparative analysis is presented
under various conditions.
