Transfer learning for the classification of video-recorded crowd movements
Mounir Bendali-Braham, Jonathan Weber, Germain Forestier, Lhassane Idoumghar, Pierre-Alain Müller · 2019
The automatic recognition of a crowd movement captured by a CCTV camera can be of considerable help to security forces whose mission is to ensure the safety of people on the public area. In this context, we propose to fine-tune a model from the TwoStream Inflated 3D architecture, pre-trained on the ImageNet and the Kinetics source datasets, to classify video sequences of crowd movements from the Crowd-11 target dataset. The evaluation of our model demonstrates its superiority over the state-of-the-art in terms of classification accuracy.