Authors:
Pau Climent-Pérez
1
;
Alexandre Mauduit
2
;
Dorothy N. Monekosso
3
and
Paolo Remagnino
1
Affiliations:
1
Kingston University, United Kingdom
;
2
École Nationale Supérieure d'Ingéniers de Caen (ENSICAEN), France
;
3
University of the West of England, United Kingdom
Keyword(s):
Tracklet Exploitation, Tracklet Plot, Bag-of-Words, Kmeans, Video Analytics, Crowd Analytics, Video Surveillance.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pervasive Smart Cameras
;
Video Surveillance and Event Detection
Abstract:
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.