Ensemble classification of video-recorded crowd movements

Abstract

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.