Assessment of the Effectiveness of the Greek Implementation. VRU-TOO Deliverable 14

The work of VRU-TOO is targeted specifically at the application of ATT for reducing risk and improving comfort (e.g. minimisation of delay) for Vulnerable Road Users, namely pedestrians. To achieve this, the project operates at three levels. At the European level practical pilot implementations in three countries (U.K., Portugal and Greece) are linked with behavioural studies of the micro-level interaction of pedestrians and vehicles and the development of computer simulation models. At the National level, the appropriate Highway Authorities are consulted, according to their functions, for the pilot implementations and informed of the results. Finally, at the local level, the pilot project work is fitted into specfic local (municipality) policy contexts in all three pilot project sites. The present report focuses on the Elefsina pilot application in Greece and the relevant National and Local policy contexts are the following. At the National level, the ultimate responsibility for road safety and signal installations rests with the Ministry of Environment and Public Works. The Ministry is responsible for the adoption of standards and solutions for problems and also for a large number of actual installations, since local authorities lack the size and expertise to undertake such work on their own One of the project's aims is to provide information to the Ministry as to the suitability of the methods developed for aiding pedestrian movement, ultimately leading to a specification for its wider use. The Ministry is expecting to use the final results of the present study for possible modifications of its present standards for pedestrian controlled traffic signals. At the local level (Elefsina) the municipality has, in the past, pursued environmental improvements through pedestrianisation schemes in the city centre. At the same time it has developed a special traffic management policy, to solve a particularly serious problem of through traffic. A summary of the policy is contained in Appendix A and more details in a previous deliverable (Tillis, 1992). In the particular case of Elefsina pedestrian induced delay to through vehicular traffic, may form a key element in this policy ensuring at the same time, an incentive to divert to the existing bypass and enhancing pedestrian movement. The effectiveness of pedestrian detection techniques tested in the pilot, will provide valuable information on the future implementation of the policy. Thus, the Elefsina Pilot Project operates at the same time on three levels: It provides a basis, in combination with the other two pilot project sites, for comparing the effects of pedestrian detection on pedestrian safety and comfort at a European level. It provides information to the National authorities (Ministry of Environment and Public Works) for their standards setting, scheme design and implementation tasks. It fits into a comprehensive plan at the local level for effecting environmental improvements and enhancing pedestrian amenity and comfort at the same time. In addition, an investigation into the capabilities of pedestrian detectors to function as data collection devices, was performed. The data 'quality gap' betweenvehicular and pedestrian tr&c may be closed with the utilisation of microwave pedestrian detectors, providing a more solid foundation for the planning for total person movement through networks (vehicle occupants, public transport passengers, pedestrians). This the second deliverable issued for Elefsina and comprises of the main section which contains a description of the work undertaken, the results and a number of appendices serving as background material in support of the statements in the main text

Exploring Human Vision Driven Features for Pedestrian Detection

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are first to design a local, statistical multi-channel descriptorin order to incorporate both color and gradient information. Second, we introduce a multi-direction and multi-scale contrast scheme based on grid-cells in order to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons w.r.t. statistical descriptors, contrast measurements, and scale structures. This way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

Taking a look at small-scale pedestrians and occluded pedestrians

Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech)