Authors:
Rita Delussu
;
Lorenzo Putzu
and
Giorgio Fumera
Affiliation:
University of Cagliari, Piazza D’Armi, Cagliari, Italy Department of Electrical and Electronic Engineering, Piazza D’Armi, 09123 Cagliari, Italy
Keyword(s):
Crowd Counting, Crowd Density Estimation, Cross-scene Evaluation, Video Surveillance.
Abstract:
Crowd counting and density estimation are useful but also challenging tasks in many video surveillance systems, especially in cross-scene settings with dense crowds, if the target scene significantly differs from the ones used for training. This also holds for methods based on Convolutional Neural Networks (CNNs) which have recently boosted the performance of crowd counting systems, but nevertheless require massive amounts of annotated and representative training data. As a consequence, when training data is scarce or not representative of deployment scenarios, also CNNs may suffer from over-fitting to a different extent, and may hardly generalise to images coming from different scenes. In this work, we focus on real-world, challenging application scenarios when no annotated crowd images from a given target scene are available, and evaluate the cross-scene effectiveness of several regression-based state-of-the-art crowd counting methods, including CNN-based ones, through extensive cr
oss-data set experiments. Our results show that some of the existing CNN-based approaches are capable of generalising to target scenes which differ from the ones used for training in the background or lighting conditions, whereas their effectiveness considerably degrades under different perspective and scale.
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