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.
Ensemble learning methods often improve results in problems addressed by single Machine Learning models. In this work, we apply Ensemble Learning on video-recorded crowd movements. First, we build Ensembles of homogeneous Convolutional Neural Networks (CNN) to compare their performance on the Crowd-11 dataset and show the gain of performance demonstrated by Ensembles compared to single CNN models. Secondly, we evaluate all the possible combinations of these homogeneous Ensembles to build a global Ensemble of heterogeneous models, and we analyze the combination of Ensembles that achieves the best results. Our experiments reveal that Ensemble classification often obtains better results than single models and combining different Ensembles can make the predictions accuracy even better.
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