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In this paper we propose ResnetCrowd, a deep residual architecture forsimultaneous crowd counting, violent behaviour detection and crowd densitylevel classification. To train and evaluate the proposed multi-objectivetechnique, a new 100 image dataset referred to as Multi Task Crowd isconstructed. This new dataset is the first computer vision dataset fullyannotated for crowd counting, violent behaviour detection and density levelclassification. Our experiments show that a multi-task approach boostsindividual task performance for all tasks and most notably for violentbehaviour detection which receives a 9\% boost in ROC curve AUC (Area under thecurve). The trained ResnetCrowd model is also evaluated on several additionalbenchmarks highlighting the superior generalisation of crowd analysis modelstrained for multiple objectives.

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification