Authors
Ahmad Alia
Mohammed Maree
Mohcine Chraibi
Conference
IEEE/ACS 19th International Conference on Computer Systems and Applications
Location
Abu Dhabi, United Arab Emirates
Pages From

Abstract

Deep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data [1]. As a result, deep learning is widely applied in the human crowd analysis domain [2]. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.