InSight: Monitoring the State of the Driver in Low-Light Using Smartphones - Janveja, Ishani; Nambi, Akshay; Bannur, Shruthi; Gupta, Sanchit; Padmanabhan, Venkat (Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UBICOMP 2020), 2020)
Road safety is a major public health issue across the globe and over two-thirds of the road accidents occur at nighttime under low-light conditions or darkness. The state of the driver and her/his actions are the key factors impacting road safety. How can we monitor these in a cost-effective manner and in low-light conditions? RGB cameras present in smartphones perform poorly in low-lighting conditions due to lack of information captured. Hence, existing monitoring solutions rely upon specialized hardware such as infrared cameras or thermal cameras in low-light conditions, but are limited to only high-end vehicles owing to the cost of the hardware. We present InSight, a windshield-mounted smartphone-based system that can be retrofitted to the vehicle to monitor the state of the driver, specifically driver fatigue (based on frequent yawning and eye closure) and driver distraction (based on their direction of gaze). Challenges arise from designing an accurate, yet low-cost and non-intrusive system to continuously monitor the state of the driver. In this paper, we present two novel and practical approaches for continuous driver monitoring in low-light conditions: (i) Image synthesis: enabling monitoring in low-light conditions using just the smartphone RGB camera by synthesizing a thermal image from RGB with a Generative Adversarial Network, and (ii) Near-IR LED: using a low-cost near-IR (NIR) LED attachment to the smartphone, where the NIR LED acts as a light source to illuminate the driver's face, which is not visible to the human eyes, but can be captured by standard smartphone cameras without any specialized hardware. We show that the proposed techniques can capture the driver's face accurately in low-lighting conditions to monitor driver's state. Further, since NIR and thermal imagery is significantly different than RGB images, we present a systematic approach to generate labelled data, which is used to train existing computer vision models. We present an extensive evaluation of both the approaches with data collected from 15 drivers in controlled basement area and on real roads in low-light conditions. The proposed NIR LED setup has an accuracy (Fl-score) of 85% and 93.8% in detecting driver fatigue and distraction, respectively in low-light.