Detecting events in crowded scenes using tracklet plots

The main contribution of this paper is a compact representation of the ‘short tracks’ or tracklets present in a time window of a given video input, which allows to analyse and detect different crowd events. To proceed, first, tracklets are extracted from a time window using a particle filter multi-target tracker. After noise removal, the tracklets are plotted into a square image by normalising their lengths to the size of the image. Different histograms are then applied to this compact representation. Thus, different events in a crowd are detected via a Bag-of-words modelling. Novel video sequences, can then be analysed to detect whether an abnormal or chaotic situation is present. The whole algorithm is tested with our own dataset, also introduced in the paper.