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.
In our preliminary work @cite_11 towards designing an efficient CAR model suitable for detecting distracted pedestrian activities, we proposed a novel CAR technique called DFAM. We also undertook a preliminary evaluation of DFAM primarily in a personalized setting, where both training and testing of DFAM were conducted using the same participants' motion data. In this paper, we significantly expand our DFAM evaluation in a personalized setting, and comprehensively evaluate a generalized setting where the training and testing are conducted on disjoint sets of participants. Such a generalized evaluation can validate if DFAM can accurately detect distracted pedestrian activities even when personalized training data from a user is not available. We were also able to significantly reduce the resource footprint of our initial DFAM implementation by designing a hierarchical activity recognition model, presented and evaluated in Section .
@cite_11
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Pedestrian safety continues to be a significant con- cern in urban communities with distraction being one of the main contributing factor behind serious accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate recognition of pedestrian distractions in real-time given the memory, computation and com- munication limitations of these devices, however, remains a key technical challenge in the design of such systems. Earlier research efforts in this direction have primarily focused on achieving high distraction detection accuracy, resulting in techniques that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not real- time, or require specialized hardware and thus less likely to be adopted by most users. Our goal in this paper is to design a pedestrian distraction detection technique that overcomes some of these shortcomings (of existing techniques) and achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption.