Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.

Existing tracking methods vary strongly in their approach and therefore have different strengths and weaknesses. For example, a single tracking algorithm may be good at handling variations in illumination, but does not cope well with deformation. Hence, their failures can occur on entirely different time intervals on the same sequence. One possible solution for overcoming limitations of a single tracker and for benefitting from individual strengths, is to run a set of tracking algorithms in parallel and fuse their outputs. But in general, tracking algorithms are not designed to receive feedback from a higher level fusion strategy or require a high degree of integration between individual levels. Towards this end, we introduce a fusion strategy serving the purpose of online single object tracking, for which no knowledge about individual tracker characteristics is needed. The key idea is to combine several independent and heterogeneous tracking approaches and to robustly identify an outlier subset based on the "Median Absolute Deviations" (MAD) measure. The MAD fusion strategy is very generic and only requires frame-based object bounding boxes as input. Thus, it can work with arbitrary tracking algorithms. Furthermore, the MAD fusion strategy can also be applied for combining several instances of the same tracker to form a more robust ensemble for tracking an object. The evaluation is done on public available datasets. With a set of heterogeneous, commonly used trackers we show that the proposed MAD fusion strategy improves the tracking results in comparison to a classical combination of parallel trackers and that the tracker ensemble helps to deal with the initialization uncertainty of a single tracker