Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal 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 and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that 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 that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices.
Several research efforts in the literature have employed mobile and or wearable devices, and data available from them, for improving pedestrian safety. @cite_42 utilized the rear camera of the smartphone to detect vehicles approaching a distracted user (or pedestrian) in order to promptly deliver a danger alert or notification. @cite_2 used image processing techniques and multi-sensor (barometer, accelerometer and gyroscope) information on smartphones to detect surrounding objects. Similarly, @cite_29 used real time video processing of road traffic to help partially sighted pedestrians in spotting obstacles on their path. @cite_45 is another proposal which applied image processing techniques on a smartphone camera feed to find obstacles in a user's path, however unlike @cite_29 , SpareEye is able to track multiple obstacles simultaneously. One significant drawback of all these proposals is that they employ costly and resource-intensive image capture and processing techniques, which can adversely impacts the performance and battery-life of mobile devices and thus their chances of being adopted by users. Reliance on the smartphone's camera, also restricts the ability of these techniques to operate when the camera is obstructed, for example, in a user's pocket.
@cite_45 @cite_29 @cite_42 @cite_2
2027120765 1490526505 2090340221 1966120703
Using mobile phones while walking for activities that require continuous focus on the screen, such as texting, has become more and more popular in the last years. To avoid colliding with obstacles, such as lampposts and pedestrians, focus has to be taken off the screen in regular intervals. In this paper we introduce SpareEye, an Android application that warns the smartphone user from obstacles in her way. We use only the camera of the phone and no special hardware, ensuring that it requires minimal effort from the user to use the application during everyday life. Experimental results show that we can detect obstacles with high accuracy, with only some false positives and few false negatives. In this paper, we present a real-time obstacle detection system for the mobility improvement for the visually impaired using a handheld Smartphone. Though there are many existing assistants for the visually impaired, there is not a single one that is low cost, ultra-portable, non-intrusive and able to detect the low-height objects on the floor. This paper proposes a system to detect any objects attached to the floor regardless of their height. Unlike some existing systems where only histogram or edge information is used, the proposed systemcombines both cues and overcomes some limitations of existing systems. The obstacles on the floor in front of the user can be reliably detected in real time using the proposed system implemented on a Smartphone. The proposed system has been tested in different types of floor conditions and a field trial on five blind participants has been conducted. The experimental results demonstrate its reliability in comparison to existing systems. Research in social science has shown that mobile phone conversations distract users, presenting a significant impact to pedestrian safety; for example, a mobile phone user deep in conversation while crossing a street is generally more at risk than other pedestrians not engaged in such behavior. We propose WalkSafe, an Android smartphone application that aids people that walk and talk, improving the safety of pedestrian mobile phone users. WalkSafe uses the back camera of the mobile phone to detect vehicles approaching the user, alerting the user of a potentially unsafe situation; more specifically WalkSafe i) uses machine learning algorithms implemented on the phone to detect the front views and back views of moving vehicles and ii) exploits phone APIs to save energy by running the vehicle detection algorithm only during active calls. We present our initial design, implementation and evaluation of the WalkSafe App that is capable of real-time detection of the front and back views of cars, indicating cars are approaching or moving away from the user, respectively. WalkSafe is implemented on Android phones and alerts the user of unsafe conditions using sound and vibration from the phone. WalkSafe is available on Android Market. Accident detection and alarm system is very important to detect possible accidents or dangers for the peoples using their mobile devices while walking, i.e., distracted walking. In this paper, we introduce an automatic accident detection and alarm system, called AutoADAS, which is fully implemented and tested on the real mobile devices. The proposed system can be activated either manually or automatically when user walks. Under the manual mode, user activates the system before distracted walking while under the automatic mode, a "user behaviour profiling" module is used to recognize (distracted) walking behaviours and an "object detection" module is activated. Using image processing and camera field of view (FOV), the distance and angle between the user and detected objects are estimated and then applied to identify whether any potential accidents can happen. The "accident analysis and prediction" module includes: temporal alarm that inputs the user's walking speed and distance with respect to the detected objects and outputs temporal accident prediction; spatial alarm that inputs the user's walking direction and angle with respect to the detected objects and outputs spatial accident prediction. Once the proposed system positively predicts a potential accident, the "alarm and suggestion" module alerts the user with text, sound or vibration.