  In this paper we propose ResnetCrowd, a deep residual architecture for
simultaneous crowd counting, violent behaviour detection and crowd density
level classification. To train and evaluate the proposed multi-objective
technique, a new 100 image dataset referred to as Multi Task Crowd is
constructed. This new dataset is the first computer vision dataset fully
annotated for crowd counting, violent behaviour detection and density level
classification. Our experiments show that a multi-task approach boosts
individual task performance for all tasks and most notably for violent
behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the
curve). The trained ResnetCrowd model is also evaluated on several additional
benchmarks highlighting the superior generalisation of crowd analysis models
trained for multiple objectives.
