Towards a Practical Pedestrian Distraction Detection Framework using Wearables

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

Pedestrian detection from a static camera

Pedestrian detection is a practical and relatively new topic of computer vision. It is widely applied to surveillance, traffic detector and environmental protection. This project focuses on the design and implementation of a pedestrian detection system. The system consists of four modules: movemen

Low-power pedestrian detection system on FPGA

Pedestrian detection is one of the key problems in the emerging self-driving car industry. In addition, the Histogram of Gradients (HOG) algorithm proved to provide good accuracy for pedestrian detection. Many research works focused on accelerating HOG algorithm on FPGA(Field-Programmable Gate Array) due to its low-power and high-throughput characteristics. In this paper, we present an energy-efficient HOG-based implementation for pedestrian detection system on a low-cost FPGA system-on-chip platform. The hardware accelerator implements the HOG computation and the Support Vector Machine classifier, the rest of the algorithm is mapped to software in the embedded processor. The hardware runs at 50 Mhz (lower frequency than previous works), thus achieving the best pixels processed per clock and the lower power design

VRU-TOO: Micro-Level Behavioural and Conflict Changes in the VRU-TOO Pilot Projects. Deliverable 15.

This report describes the results of an evaluation of pilot project pedestrian detection systems installed in three different European cities at in total six different sites. The implemented systems had the improvement of safety and comfort of pedestrians as objectives. The systems provided early detection of pedestrians approaching the crossing facility and detection of the presence of pedestrian on the crossing facility, allowing the onset of a pedestrian green phase or an extension of such a green phase. The evaluation involved the registration of pedestrian behaviour and pedestrian-vehicle encounters and conflicts. Pedestrian behaviour was recorded on videotape, conflicts were scored on the spot by trained observers. The behaviour recorded on videotape was later analysed using approach speed, nonnative behaviour and appropriate use of the crossing facilities as main indicators. In addition, pedestrian signal settings were recorded for each crossing. The evaluation design used a beforelafter measurement design with the after measurements being taken at least two weeks after the system implementation. The results indicated that although red light violations were reduced at some sites, they remained at a high level. The implementations had some positive effects on the normative behaviour of pedestrians. The percentage of pedestrians getting involved in encounters with motorized traffic was reduced at one site, increased at another and remained unchanged at the other sites. A significant reduction in conflicts was observed at several sites, but at other sites conflict occurrence remained unchanged. The system implementations had a very distinct positive effect on pedestrian delay. Required waiting times were reduced at all but one site and at some sites the reductions were substantial. Pedestrian comfort was also improved by an increase in the percentage of pedestrians arriving durhg the pedestrian green phase and the percentage being able to complete their crossing during pedestrian green. In summary, the evaluation study demonstrated that some safety effects and substantial effect on comfort were achieved by the implementation of the systems. The effects can be further optimized by selecting sites that fulfil specific requirements for successful implementation. Red light violation by pedestrian remains a serious safety problem and further studies should be undertaken how further reductions can be achieved by optimizing signal settings

Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-design platform has become a key strategic element for developing hardware/software integrated systems. In this paper, we propose a new design flow for building a scalable co-design platform on FPGA-based system-on-chip. We employ an integrated approach to implement a histogram oriented gradients (HOG) and a support vector machine (SVM) classification on a programmable device for pedestrian tracking. Not only was hardware resource analysis reported, but the precision and success rates of pedestrian tracking on nine open access image data sets are also analysed. Finally, our proposed design flow can be used for any real-time image processingrelated products on programmable ZYNQ-based embedded systems, which benefits from a reduced design time and provide a scalable solution for embedded image processing products

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios