Pattern Recognition ( IF 7.196 ) Pub Date : 2019-11-29 , DOI: 10.1016/j.patcog.2019.107140 Hazar Mliki; Fatma Bouhlel; Mohamed Hammami
This research paper introduces a new approach for human activity recognition from UAV-captured video sequences. The proposed approach involves two phases: an offline phase and an inference phase. A scene stabilization step is performed together with these two phases. The offline phase aims to generate the human/non-human model as well as a human activity model using a convolutional neural network. The inference phase makes use of the already generated models in order to detect humans and recognize their activities. Our main contribution lies in adapting the convolutional neural networks, normally dedicated to the classification task, to detect humans. In addition, the classification of human activities is carried out according to two scenarios: An instant classification of video frames and an entire classification of the video sequences. Relying on an experimental evaluation of the proposed methods for human detection and human activity classification on the UCF-ARG dataset, we validated not only these contributions but also the performance of our methods compared to the existing ones.