An Intelligent Cooperative Visual Sensor Network for Urban Mobility

Leone, Giuseppe Riccardo; Moroni, Davide; Pieri, Gabriele; Petracca, Matteo; Salvetti, Ovidio; Azzarà, Andrea; Marino, Francesco

doi:10.3390/s17112588

Cited by 36 publications

(14 citation statements)

References 57 publications

(51 reference statements)

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“…Technology and sensors have become an important pillar of a model of city and community management. Already in 2017 there were remarks about intelligent urban infrastructure illustrated by the case of intelligent transportation system, which will enable to use existing road networks both in public or private transport more economically by means of visual sensors (Leone et al, 2017;Ruutu et al, 2017;Stanković et al, 2017). Such sensors allow to analyze individual road phenomena such as rush hours or level of congestion in order to optimize transport management or parking systems in the most efficient way.…”

Section: Literature Reviewmentioning

confidence: 99%

Is It a Smart City a Creative Place?

Gwiaździński

Kaczorowska–Spychalska

Pinto

2020

Creativity Studies

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Along this article we share our research in the field of urban creativity, in particular on how smart cities are becoming more and more independent and developing a spirit of sustainable autonomy that somehow creates creative opportunities in terms of memory and cultural identity. Our current article raises the issue of how can smart cities affect the creative process? We believe that creativity becomes a process linked into a digital world and becomes much more interactive. That is why new ways of artistic and digital expression can be welcomed by those who are used to new technologies, which daily influence human activity in the space of the city. In other words, with the use of the existing technology inside the cities and their interconnections with other cities we can conceive creative strategies that will contribute to preserve the memory as well as the cultural and creative identity of a people. Video-mapping is precisely one of those creative strategies, once it will directly interact between the real dimension and the virtual dimension. The use of video-mapping, as an element of covering the facades of buildings, can somehow help to make the streets more dynamic and transform them into other atmospheres. The city becomes part of the third dimension and people are interacting between the real and the virtual. The management of the urban space has been gradually changing and following the technological advance. Mobility and sustainability is one of the key factors in which a smart city has invested the most. Now is the time to invest in a relationship between the city and the people, making it more humane and giving space for creativity.

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Section: Literature Reviewmentioning

confidence: 99%

Is It a Smart City a Creative Place?

Gwiaździński

Kaczorowska–Spychalska

Pinto

2020

Creativity Studies

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“…To overcome the problem described above, there currently exist various systems, such as those proposed in [2,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. In some studies, the parking management system problem was addressed by using smart devices [24,25,26,27].…”

Section: Related Workmentioning

confidence: 99%

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Nurullayev

Lee

2019

Sensors

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The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

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“…Several wireless technologies have been proposed to achieve reliable communication [ 40 , 41 ] and applied to robots applications [ 42 , 43 , 44 , 45 ]. In the smart transportation system, detection and ranging sensors can allow robust detection for a target and measurement of their speed, distance and motion direction [ 46 , 47 , 48 ]. In this paper, we assume that the physical layer of the sensing/communication part of the multi-agent system is well developed, and we concentrate on the algorithm of the multi-agent system especially for the shepherding task.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Shepherding Behaviors of Multiple Target Steering Robots

Lee

Kim

2017

Sensors

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This paper presents a distributed coordination methodology for multi-robot systems, based on nearest-neighbor interactions. Among many interesting tasks that may be performed using swarm robots, we propose a biologically-inspired control law for a shepherding task, whereby a group of external agents drives another group of agents to a desired location. First, we generated sheep-like robots that act like a flock. We assume that each agent is capable of measuring the relative location and velocity to each of its neighbors within a limited sensing area. Then, we designed a control strategy for shepherd-like robots that have information regarding where to go and a steering ability to control the flock, according to the robots’ position relative to the flock. We define several independent behavior rules; each agent calculates to what extent it will move by summarizing each rule. The flocking sheep agents detect the steering agents and try to avoid them; this tendency leads to movement of the flock. Each steering agent only needs to focus on guiding the nearest flocking agent to the desired location. Without centralized coordination, multiple steering agents produce an arc formation to control the flock effectively. In addition, we propose a new rule for collecting behavior, whereby a scattered flock or multiple flocks are consolidated. From simulation results with multiple robots, we show that each robot performs actions for the shepherding behavior, and only a few steering agents are needed to control the whole flock. The results are displayed in maps that trace the paths of the flock and steering robots. Performance is evaluated via time cost and path accuracy to demonstrate the effectiveness of this approach.

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An Intelligent Cooperative Visual Sensor Network for Urban Mobility

Cited by 36 publications

References 57 publications

Is It a Smart City a Creative Place?

Is It a Smart City a Creative Place?

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Autonomous Shepherding Behaviors of Multiple Target Steering Robots

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