Video footage of real crowded scenes still poses severe challenges for automated surveillance. This paper evaluates clustering methods for finding independent dominant motion fields for an observation period based on a recently published real-time optical flow algorithm. We focus on self-tuning spectral clustering and Isomap combined with k-means. Several combinations of feature vector normalizations and distance measures (Euclidean, Mahanalobis and a general additive distance) are evaluated for four image sequences including three publicly available crowd datasets. Evaluation is based on mean accuracy obtained by comparison with a manually defined ground truth clustering. For every dataset at least one approach correctly classified more than 95% of the flow vectors without extra tuning of parameters, providing a basis for an automatic analysis after a view-dependent setup.
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