Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

hpDJ: An automated DJ with floorshow feedback

Many radio stations and nightclubs employ Disk-Jockeys (DJs) to provide a continuous uninterrupted stream or “mix” of dance music, built from a sequence of individual song-tracks. In the last decade, commercial pre-recorded compilation CDs of DJ mixes have become a growth market. DJs exercise skill in deciding an appropriate sequence of tracks and in mixing 'seamlessly' from one track to the next. Online access to large-scale archives of digitized music via automated music information retrieval systems offers users the possibility of discovering many songs they like, but the majority of consumers are unlikely to want to learn the DJ skills of sequencing and mixing. This paper describes hpDJ, an automatic method by which compilations of dance-music can be sequenced and seamlessly mixed by computer, with minimal user involvement. The user may specify a selection of tracks, and may give a qualitative indication of the type of mix required. The resultant mix can be presented as a continuous single digital audio file, whether for burning to CD, or for play-out from a personal playback device such as an iPod, or for play-out to rooms full of dancers in a nightclub. Results from an early version of this system have been tested on an audience of patrons in a London nightclub, with very favourable results. Subsequent to that experiment, we designed technologies which allow the hpDJ system to monitor the responses of crowds of dancers/listeners, so that hpDJ can dynamically react to those responses from the crowd. The initial intention was that hpDJ would monitor the crowd’s reaction to the song-track currently being played, and use that response to guide its selection of subsequent song-tracks tracks in the mix. In that version, it’s assumed that all the song-tracks existed in some archive or library of pre-recorded files. However, once reliable crowd-monitoring technology is available, it becomes possible to use the crowd-response data to dynamically “remix” existing song-tracks (i.e, alter the track in some way, tailoring it to the response of the crowd) and even to dynamically “compose” new song-tracks suited to that crowd. Thus, the music played by hpDJ to any particular crowd of listeners on any particular night becomes a direct function of that particular crowd’s particular responses on that particular night. On a different night, the same crowd of people might react in a different way, leading hpDJ to create different music. Thus, the music composed and played by hpDJ could be viewed as an “emergent” property of the dynamic interaction between the computer system and the crowd, and the crowd could then be viewed as having collectively collaborated on composing the music that was played on that night. This en masse collective composition raises some interesting legal issues regarding the ownership of the composition (i.e.: who, exactly, is the author of the work?), but revenue-generating businesses can nevertheless plausibly be built from such technologies

Microclimate variations between semienclosed and open sections of a marathon route

The Hong Kong Standard Chartered Marathon, held annually, is one of the most popular international marathon events. Its primarily urban environmental setting characterized by high-density urban areas, semienclosed tunnels, and suspension bridges, together with the herds of runners, has an influence on the microclimate along the marathon course. This study focused on assessing and comparing variations in temperature and vapour pressure (vis-à-vis relative humidity) against the crowd of runners, or the herd effects, in two different environmental settings along the marathon course: semienclosed (a tunnel) versus open space (a suspension bridge). A series of small iButtons were deployed at strategic locations along the course to undertake minute-by-minute measurements of temperature and relative humidity. It was found that herd effects of varying degrees were present in both semienclosed and open settings. Various environmental differences also played a role in ameliorating or amplifying the climatological effects of the herd of runners. Our study suggests that microclimate variations in different environmental settings and crowd conditions could have an impact on runners. This new knowledge can inform the design of marathon routes. It also establishes the feasibility of employing the iButton logging sensors for widespread deployment and monitoring of meteorological situations

Instantaneous vehicle fuel consumption estimation using smartphones and Recurrent Neural Networks

The high level of air pollution in urban areas, caused in no small extent by road transport, requires the implementation of continuous and accurate monitoring techniques if emissions are to be minimized. The primary motivation for this paper is to enable fine spatiotemporal monitoring based on crowd sensing, whereby the instantaneous fuel consumption of a vehicle is estimated using smartphone measurements. To this end, a surrogate method based on indirect monitoring using Recurrent Neural Networks (RNNs) that process a smartphone's GPS position, speed, altitude, acceleration and number of visible satellites is proposed. Extensive field trials were conducted to gather smartphone and fuel consumption data at a wide range of driving conditions. Two different RNN types were explored, and a parametric analysis was performed to define a suitable architecture. Various training methods for tuning the RNN were evaluated based on performance and computational burden. The resulting estimator was compared with others found in the literature, and the results confirm its superior performance. The potential impact of the proposed method is noteworthy as it can facilitate accurate monitoring of in-use vehicle fuel consumption and emissions at large scales by exploiting available smartphone measurements.</p

Lake Ice Monitoring with Webcams and Crowd-Sourced Images

Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and different lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing web sites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.Comment: Accepted for ISPRS Congress 2020, Nice, Franc