2018/11/09 by Nisha Vinayaga-Sureshkanth, Anindya Maiti, Vinayaga-Sureshkanth, Nisha +11
Computer Science · Engineering · Medicine · #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Injury Epidemiology and Prevention #Traffic and Road Safety #cs.HC
paper · pdf · doi:10.48550/arxiv.1811.04797
arXiv admin note: substantial text overlap with arXiv:1710.03755
arxiv created 2018/11/09 · openalex publication_date 2018/11/09 · arxiv updated 2018/11/13 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Distracted pedestrians, like distracted drivers, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing such frameworks is possible with the availability of sophisticated mobile and wearable devices equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge is accurate recognition of distractions with minimal resources in real-time given the computation and communication limitations of these devices. Several recently published works improve distracted pedestrian safety by leveraging on complex activity recognition frameworks using mobile and wearable sensors to detect pedestrian distractions. Their primary focus, however, was to achieve high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient and real-time pedestrian distraction detection technique that overcomes some of these shortcomings. We demonstrate its practicality by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from participants in realistic pedestrian experiments. Using these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy and energy consumption compared to some other techniques in the literature.