Can shared surfaces be safely negotiated by blind and partially sighted people?

‘Shared Space’ schemes are designed to remove the physical distinction between pedestrian space and traffic space in the street environment to encourage more pedestrians to use the area. They may also make it easier for people with wheelchairs, prams or similar to negotiate the space. However, by removing the kerbs, blind and partially sighted people lose one of the key references that they normally use to know they are in a safe space away from vehicles and to navigate around the area. This study is intended to understand what people with visual impairments need from a surface to make it clearly detectable, given that it should not be a barrier to progress for people with other mobility limitations. With this information, some surfaces were tested to determine their suitability as a delineator. Approach and/or Methodology An experimental approach was adopted. People with mobility impairments and blind and partially sighted people were recruited. All participants used the normal street environment unaccompanied. The blind and partially sighted participants included people who use a guide dog, those who use a long cane and those who use no assistive device. The people with mobility impairments all used some form of mobility aid for example walking stick or wheelchair. The tests were run in the pedestrian testing facility PAMELA at UCL. The top surface of the test facility was predominantly concrete paving slab, but with test surfaces discretely located. The task for all participants was to travel from one designated place in the test area to another. For some of these trials the participant would encounter one of the test surfaces, but on other trials they would not. After each trial the participants were asked to rate how easy it was to detect a change in surface, or how easy it was to pass over the surface. The different surfaces included blister paving, corduroy paving, a central delineator, slopes, roughened surfaces, and traditional kerb upstands of different heights. Results or Expected Results None of the 400mm wide surfaces was detected by all participants. Changes in level through slopes were considered both positively and negatively, some people asking for steeper gradients and some less steep. Kerb heights below 60mm were not reliably detectable by blind or partially sighted people and are an obstacle to people in wheelchairs. Further tests on more surfaces are in process and the results will be incorporated into this paper. Conclusion Early suggestions for detectable surfaces – proposed in UK schemes - have been either a barrier to people with mobility impairments, or difficult to detect for blind and partially sighted people or both. The work presented in this paper shows the difficulty in finding a suitable dual purpose surface, yet clarifies the design requirements for shared space delineators for people with mobility impairments and blind or partially sighted people. This work has reinforced the point that 400mm width is insufficient to be used as a tactile surface. Further conclusions will be made after the additional surface tests. Topic Code: Ca C. Accessibility concerns and solutions for those with cognitive and sensory impairment a. Pedestrian safety at crossings and intersection

Socially Constrained Structural Learning for Groups Detection in Crowd

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201

Humans do not always act selfishly: social identity and helping in emergency evacuation simulation

To monitor and predict the behaviour of a crowd, it is imperative that the technology used is based on an accurate understanding of crowd psychology. However, most simulations of evacuation scenarios rely on outdated assumptions about the way people behave or only consider the locomotion of pedestrian movement. We present a social model for pedestrian simulation based on self-categorisation processes during an emergency evacuation. We demonstrate the impact of this new model on the behaviour of pedestrians and on evacuation times. In addition to the Optimal Steps Model for locomotion, we add a realistic social model of collective behaviour

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio