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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We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches. Micro-Doppler (MD)-based target classification capabilities of automotive radars can provide high reliability and short latency to future active safety automotive features. A large number of pedestrians surrounding a vehicle in practical urban scenarios mandate prioritization of their treatment level. Distinguishing between relevant pedestrians that are crossing the street or are within the vehicle path and those that are on the sidewalks and are moving along the vehicle route can significantly minimize the number of vehicle-to-pedestrian accidents. This work proposes a novel technique for estimating a pedestrian direction of motion that treats pedestrians as complex distributed targets and utilizes their MD radar signatures. The MD signatures are shown to be indicative of pedestrian direction of motion, and a supervised regression is used to estimate the mapping between the directions of motion and the corresponding MD signatures. In order to achieve higher regression performance, a state-of-the-art sparse dictionary learning-based feature extraction algorithm was adopted from the field of computer vision by drawing a parallel between the Doppler effect and the video temporal gradient. The performance of the proposed approach is evaluated in practical automotive scenario simulations, where a walking pedestrian is observed by a multiple-input multiple-output automotive radar with a two-dimensional rectangular array. The simulated data was generated using the statistical Boulic-Thalman human locomotion model. Accurate direction of motion estimation was achieved by using support vector regression and multilayer perceptron-based regression algorithms. The results show that the direction estimation error is less than 10° in 95 of the tested cases for pedestrian at a range of 100 m from the radar. Future vehicle systems for active pedestrian safety will not only require a high recognition performance but also an accurate analysis of the developing traffic situation. In this paper, we present a study on pedestrian path prediction and action classification at short subsecond time intervals. We consider four representative approaches: two novel approaches (based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only (a Kalman filter and its extension to interacting multiple models). In experiments using stereo vision data obtained from a vehicle, we investigate the accuracy of path prediction and action classification at various time horizons, the effect of various errors (image localization, vehicle egomotion estimation), and the benefit of the proposed approaches. The scenario of interest is that of a crossing pedestrian, who might stop or continue walking at the road curbside. Results indicate similar performance of the four approaches on walking motion, with near-linear dynamics. During stopping, however, the two newly proposed approaches, with nonlinear and or higher order models and augmented motion features, achieve a more accurate position prediction of 10-50 cm at a time horizon of 0-0.77 s around the stopping event. This paper addresses the problem of vehicle detection using Deep Convolutional Neural Network (ConvNet) and 3D-LIDAR data with application in advanced driver assistance systems and autonomous driving. A vehicle detection system based on the Hypothesis Generation (HG) and Verification (HV) paradigms is proposed. The data inputted to the system is a point cloud obtained from a 3D-LIDAR mounted on board an instrumented vehicle, which is transformed to a Dense-depth Map (DM). The proposed solution starts by removing ground points followed by point cloud segmentation. Then, segmented obstacles (object hypotheses) are projected onto the DM. Bounding boxes are fitted to the segmented objects as vehicle hypotheses (the HG step). Finally, the bounding boxes are used as inputs to a ConvNet to classify verify the hypotheses of belonging to the category ‘vehicle’ (the HV step). In this paper, we present an evaluation of ConvNet using LIDAR-based DMs and also the impact of domain-specific data augmentation on vehicle detection performance. To train and to evaluate the proposed vehicle detection system, the KITTI Benchmark Suite was used.