scholarly journals Video Analysis of Pedestrian Movement (VAPM) under Different Lighting Conditions—Method Exploration

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4141
Author(s):  
Maria Johansson ◽  
Aliaksei Laureshyn ◽  
Mikael Nilsson
Keyword(s):  
Video Analysis ◽  
Smart City ◽  
Large Scale ◽  
Outdoor Environment ◽  
Energy Usage ◽  
Pedestrian Movement ◽  
Lighting Conditions ◽  
Methodological Triangulation ◽  
Lighting Systems ◽  
New City

When daylight hours are limited, pedestrians are dependent on appropriate outdoor lighting. Although new city lighting applications must consider both energy usage and pedestrian responses, current methods used to capture pedestrian walking behaviour during dark conditions in real settings are limited. This study reports on the development and evaluation of a video-based method that analyses pedestrians’ microscopic movements (VAPM—video analysis of pedestrian movements), including placement and speed, in an artificially lit outdoor environment. In a field study utilising between-subjects design, 62 pedestrians walked along the same path under two different lighting applications. VAPM accurately discriminated pedestrians’ microscopic movements in the two lighting applications. By incorporating methodological triangulation, VAPM successfully complemented observer-based assessments of pedestrians’ perceptions and evaluations of the two lighting applications. It is suggested that in evaluations of pedestrian responses to city lighting applications, observer-based assessments could be successfully combined with an analysis of actual pedestrian movement while walking in the lit environment. However, prior to employing a large-scale application of VAPM, the methodology needs to be further adapted for use with drones and integration into smart city lighting systems.

Research into the Effect of Photometric Flicker Event on the Perception of Office Workers

2019 ◽  
pp. 22-27
Author(s):  
Cenk Yavuz ◽  
Ceyda Aksoy Tırmıkç ◽  
Burcu Çarklı Yavuz
Keyword(s):  
Experimental Work ◽  
Psychological Problems ◽  
Office Workers ◽  
Questionnaire Study ◽  
Fast Growing ◽  
Lighting Conditions ◽  
Lighting Systems ◽  
Enormous Number

Today the number of office workers has reached to an enormous number due to the fast-growing technology. Most of these office workers spend long hours in enclosed spaces with little/no daylight penetration. The lack of daylight causes physiological and psychological problems with the workers. At this point lighting systems become prominent as the source and the solution of the problem. Photometric flicker event which arises in the lighting systems can sometimes become visible and brings a lot of issues with it. In this paper, an experimental work has been done to investigate the effect of flicker. For this purpose, the flicker values of 3 different experiment rooms for different lighting conditions and scenarios have been measured and a questionnaire study has been carried out in the experiment rooms with 30 participants. In conclusion, the effect of the flicker event on the volunteers have been classified and some methods have been proposed not to experience flicker effects.

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation

2021 ◽  
Vol 15 (3) ◽  
pp. 1-27
Author(s):  
Yan Liu ◽  
Bin Guo ◽  
Daqing Zhang ◽  
Djamal Zeghlache ◽  
Jingmin Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Start ◽  
Weighting Scheme ◽  
Training Data ◽  
Chain Store ◽  
Useful Knowledge ◽  
Adversarial Network ◽  
Brick And Mortar ◽  
New City ◽  
Learning Machine

Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

scholarly journals Towards the Optimum Light Source Spectrum

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Andrew Chalmers ◽  
Snjezana Soltic
Keyword(s):  
Light Source ◽  
Large Scale ◽  
Source Spectrum ◽  
Light Sources ◽  
Luminous Efficacy ◽  
Light Emitting ◽  
Spectral Bands ◽  
Good Energy ◽  
Lighting Systems ◽  
Colour Rendering

This paper is concerned with designing light source spectra for optimum luminous efficacy and colour rendering. We demonstrate that it is possible to design light sources that can provide both good colour rendering and high luminous efficacy by combining the outputs of a number of narrowband spectral constituents. Also, the achievable results depend on the numbers and wavelengths of the different spectral bands utilized in the mixture. Practical realization of these concepts has been demonstrated in this pilot study which combines a number of simulations with tests using real LEDs (light emitting diodes). Such sources are capable of providing highly efficient lighting systems with good energy conservation potential. Further research is underway to investigate the practicalities of our proposals in relation to large-scale light source production.

scholarly journals A comprehensive evaluation of cache utilization characteristics in large scale WSN considering network driven cache replacement techniques

2018 ◽  
Vol 188 ◽  
pp. 05004
Author(s):  
Christos Panagiotou ◽  
Christos Antonopoulos ◽  
Stavros Koubias
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Resource Conservation ◽  
Data Sources ◽  
Data Cache ◽  
Cache Replacement ◽  
Large Scale Networks ◽  
Result Analysis

WSNs as adopted in current smart city deployments, must address demanding traffic factors and resilience in failures. Furthermore, caching of data in WSN can significantly benefit resource conservation and network performance. However, data sources generate data volumes that could not fit in the restricted data cache resources of the caching nodes. This unavoidably leads to data items been evicted and replaced. This paper aims to experimentally evaluate the prominent caching techniques in large scale networks that resemble the Smart city paradigm regarding network performance with respect to critical application and network parameters. Through respective result analysis valuable insights are provided concerning the behaviour of caching in typical large scale WSN scenarios.

scholarly journals Application Research of Key Frames Extraction Technology Combined with Optimized Faster R-CNN Algorithm in Traffic Video Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhi-guang Jiang ◽  
Xiao-tian Shi
Keyword(s):  
Video Analysis ◽  
Management System ◽  
Large Scale ◽  
Data Communication ◽  
Transportation System ◽  
Extraction Technology ◽  
Transportation Management ◽  
Key Frame Extraction ◽  
Original Algorithm ◽  
Key Frame

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.

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