  Understanding crowd behavior in video is challenging for computer vision.
There have been increasing attempts on modeling crowded scenes by introducing
ever larger property ontologies (attributes) and annotating ever larger
training datasets. However, in contrast to still images, manually annotating
video attributes needs to consider spatiotemporal evolution which is inherently
much harder and more costly. Critically, the most interesting crowd behaviors
captured in surveillance videos (e.g., street fighting, flash mobs) are either
rare, thus have few examples for model training, or unseen previously. Existing
crowd analysis techniques are not readily scalable to recognize novel (unseen)
crowd behaviors. To address this problem, we investigate and develop methods
for recognizing visual crowd behavioral attributes without any training
samples, i.e., zero-shot learning crowd behavior recognition. To that end, we
relax the common assumption that each individual crowd video instance is only
associated with a single crowd attribute. Instead, our model learns to jointly
recognize multiple crowd behavioral attributes in each video instance by
exploring multiattribute cooccurrence as contextual knowledge for optimizing
individual crowd attribute recognition. Joint multilabel attribute prediction
in zero-shot learning is inherently nontrivial because cooccurrence statistics
does not exist for unseen attributes. To solve this problem, we learn to
predict cross-attribute cooccurrence from both online text corpus and
multilabel annotation of videos with known attributes. Our experiments show
that this approach to modeling multiattribute context not only improves
zero-shot crowd behavior recognition on the WWW crowd video dataset, but also
generalizes to novel behavior (violence) detection cross-domain in the Violence
Flow video dataset.
