Framework for pedestrian walking behaviour recognition to minimize road accident

Pedestrian walking misbehaviour represents a severe problem to road safety. Therefore, pedestrian behaviour classification is a perfect solution in providing safety for both pedestrians and vehicles by exchanging movement information among entities via wireless communication. However, wireless communication has critical issues with network failure, and these issues significantly affect the communication system. Thus, the framework involved two modules for pedestrian walking behaviour classification in a vehicle-to-pedestrian (V2P) context is proposed. In the methodology, this study discloses five useful stages. Firstly, mobile phone users' irregular walking behaviour is investigated using a questionnaire to determine their options on mobile usage in the street. Secondly, four different testing scenarios are chosen to acquire pedestrian walking data using the gyroscope sensor, where the essential features were extracted and selected. Thirdly, the pedestrian's behaviour is recognized using grid optimizer in machine learning. Fourthly, four standard vectors for pedestrian walking behaviour are developed. Fifthly, the performance of the proposed classification methods is validated and evaluated against multiple scenarios and features. Two sets of real-time data are presented in this work. The first one is related to the questionnaire data, consisting of 262 respondent samples, while the second set has 263 samples of pedestrian walking signals. The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. In contrast, for chatting and running behaviour, the accuracy is 100% and 80%, respectively. This study's implication serves the safety system in the V2P context by programming the proposed framework as an application in smartphones for exchanging pedestrian information to the vehicles for avoiding accidents

Investigation of Lighting Levels for Pedestrians - Some questions about lighting levels of current lighting standards

22-23 September, 200

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

Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

Investigating use of space of pedestrians

Understanding use of space of pedestrian is important to plan/design street environments or large public transport facilities. The purpose of a series of our research is to investigate use of space of various pedestrians in a variety of environmental situations. The research is a part of PAMELA project designed to test existing and proposed pedestrian environments and street facilities (i.e. a bus stop) under controlled conditions. This paper is aimed at setting out the background of the research, and presenting a basic frame work for subsequent research. Strength of our approach is the microscopic heterogeneous approach, where each walking person is regarded different from others. Relations among characteristics of pedestrians, characteristics of facilities/ environments, and resulting actions of pedestrians are carefully examined. Conclusion suggests directions of further research