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.
Several research efforts in the literature have employed mobile and or wearable devices to improve pedestrian safety by detecting hazardous contexts using users' smartphone camera @cite_29 @cite_30 @cite_1 @cite_21 . @cite_29 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_1 used image processing techniques and multi-sensor (barometer, accelerometer and gyroscope) information on smartphones to detect surrounding objects. Similarly, @cite_21 used real time video processing of road traffic to help partially sighted pedestrians in spotting obstacles on their path. @cite_30 is another proposal which applied image processing techniques on a smartphone camera feed to simultaneously find multiple obstacles in a user's path unlike the results by @cite_21 . One significant drawback of all these proposals is that they employ costly and resource-intensive image capture and processing techniques, which can adversely impact the performance and battery-life of mobile devices, thus diminishing their chances of being continually adopted by users. Reliance on a 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_30 @cite_29 @cite_21 @cite_1
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It is well recognized that walking while using mobile phones will make people more susceptible at various risks. Existing studies to improve smartphone users' safety are mainly limited to detecting incoming vehicles. They are not able to address some more common and equally dangerous accidents such as trips, falling from stairs, platforms or falling into an open manhole. These hazards are generally caused by sudden change of ground. In this paper, we propose UltraSee, the first system that is able to detect sudden change of ground for pedestrian mobile phone users. UltraSee augments smartphones with a small ultrasonic sensor which can detect the abrupt change of distance ahead. UltraSee also leverages the context information of smartphone usage such as screen status and holding orientation to improve detection accuracy and reduce energy consumption as well as unnecessary alarms. We have carried out extensive experiments in different scenarios and by different users. The results show that UltraSee can achieve accident detection rate of 94 with false positive rate of 4.4 and reduce unnecessary alarms by 90 . In terms of energy consumption, UltraSee costs only about 20 energy compared to the existing works that only rely on smartphone cameras. Pedestrians' use of Motion Pictures Expert Group audio layer 3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel simple nonresource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three-component heuristic. The resulting acoustic hazard detection system has a very low false-positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety. In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech [P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 4, pp. 743–761, Apr. 2012], and obtains competitive results on INRIA [N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2005, pp. 886–893], ETH [A. Ess, B. Leibe, and L. V. Gool, “Depth and appearance for mobile scene analysis,” in Proc. Int. Conf. Comput. Vis ., 2007, pp. 1–8], and KITTI [A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit ., 2012, pp. 3354–3361]. Multi-camera pedestrian detection is the challenging problem in the field of surveillance video analysis. However, existing approaches may produce "phantoms" (i.e., fake pedestrians) due to the heavy occlusions in real surveillance scenario, while calibration errors and the diverse heights of pedestrians may also heavily decrease the detection performance. To address these problems, this paper proposes a robust multiple cameras pedestrian detection approach with multi-view Bayesian network model (MvBN). Given the preliminary results obtained by any multi-view pedestrian detection method, which are actually comprised of both real pedestrians and phantoms, the MvBN is used to model both the occlusion relationship and the homography correspondence between them in all camera views. As such, the removal of phantoms can be formulated as an MvBN inference problem. Moreover, to reduce the influence of the calibration errors and keep robust to the diverse heights of pedestrians, a height-adaptive projection (HAP) method is proposed to further improve the detection performance by utilizing a local search process in a small neighborhood of heights and locations of the detected pedestrians. Experimental results on four public benchmarks show that our method outperforms several state-of-the-art algorithms remarkably and demonstrates high robustness in different surveillance scenes. HighlightsA multi-view Bayesian network is proposed to model pedestrian candidates and their occlusion relationships in all views.A parameter learning algorithm is developed for MvBN by using a set of auxiliary, real-valued, and continuous variables.A height-adaptive projection is proposed to make the final detection robust to synthesis noises and calibration errors.Our approach is recognized as the best performer in five PETS evaluations from 2009 to 2013.