Crowd-Counting through a Cascaded, Multi-Task Convolutional Neural Network

Abstract

Deep learning is one of the most popular technologies and research areas in machine learning. Convolutional Neural Networks (CNNs) are a typical artificial neural network underpinning deep learning. They have been used in many fields including image recognition, natural language process, and through games such as AlphaGo. A CNN has many advantages such as efficient feature extraction, the simplicity of data format required and the small number of (hyper-)parameters that are required. This paper focuses on a particular application of deep learning: crowd counting. To address this, we apply a Multi-task, Cascaded Convolutional Neural Network (MTCNN). Compared to other models, this model has a good performance and requires a shorter inference time, with shallower network structure and smaller size. In order to demonstrate the value and feasibility of the technology and provide a friendly operating environment for users, the application was realised on both the iOS and Android platforms. A web platform was also developed to visualize the real-time data using a Firebase server.