Antoniou, Antreas and Angelov, Plamen Parvanov (2016) A general purpose intelligent surveillance system for mobile devices using deep learning. In: 2016 International Joint Conference on Neural Networks (IJCNN) :. Neural Networks (IJCNN), 2016 International Joint Conference on . IEEE, Vancouver Canada, pp. 2879-2886. ISBN 9781509006199
A_General_Purpose_Intelligent_Surveillance_System_For_Mobile_Devices_Deep_learning.pdf - Accepted Version
Download (888kB)
Abstract
In this paper the design, implementation, and evaluation of a general purpose smartphone based intelligent surveillance system is presented. It has two main elements; i) a detection module, and ii) a classification module. The detection module is based on the recently introduced approach that combines the well-known background subtraction method with the optical flow and recursively estimated density. The classification module is based on a neural network using Deep Learning methodology. Firstly, the architecture design of the convolutional neural network is presented and analyzed in the context of the four selected architectures (two of them recent successful types) and two custom modifications specifically made for the problem at hand. The results are carefully evaluated, and the best one is selected to be used within the proposed system. In addition, the system is implemented on both a PC (using Linux type OS) and on a smartphone (using Android). In addition to the compatibility with all modern Android-based devices, most GPU powered platforms such as Raspberry Pi, Nvidia Tegra X1 and Jetson run on Linux. The proposed system can easily be installed on any such device benefiting from the advantage of parallelisation for faster execution. The proposed system achieved a performance which surpasses that of a human (classification accuracy of the top 1 class >95.9% for automatic recognition of a detected object into one of the seven selected categories. For the top-2 classes, the accuracy is even higher (99.85%). That means, at least, one of the two top classes suggested by the system is correct. Finally, a number of visual examples are showcased of the system in use in both PC and Android devices.