Detecting Usage of Mobile Phones using Deep Learning Technique

Abstract

As the capabilities of mobile phones have increased, the potential of their negative use has also increased tremendously. For example, use of mobile phones while driving or in high-security zones can lead to accidents, information leaks and security breaches. In this paper, we use deep-learning algorithms viz., single shot multiBox detector (SSD) and faster-region based convolution neural network (Faster-RCNN), to detect mobile phone usage. We highlight the importance of mobile phone usage detection and the challenges involved in it. We have used a subset of State Farm Distracted Driver Detection dataset from Kaggle, which we term as KaggleDriver dataset. In addition, we have created a dataset on mobile phone usage, which we term as IITH-dataset on mobile phone usage (IITH-DMU). Although small, IITH-DMU is more generic than the KaggleDriver dataset, since it has images with higher amount of variation in foreground and background objects. Ours is possibly the first work to perform mobile-phone detection for a wide range of scenarios. On the KaggleDriver dataset, the AP at 0.5IoU is 98.97% with SSD and 98.84% with Faster-RCNN. On the IITH-DMU dataset, these numbers are 92.6% for SSD and 95.92% for Faster-RCNN. These pretrained models and the datasets are available at sites.google.com/view/mobile-phone-usage-detection