Graffiti Detection Using a Time-Of-Flight Camera

Tombari, Federico; Stefano, Luigi Di; Mattoccia, Stefano; Zanetti, Andrea

doi:10.1007/978-3-540-88458-3_58

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…There are several works aimed to combat graffiti vandalism acts. Web-based frameworks [4]- [7] rely on community participation to identify recently degraded locations while some works [8]- [10] try to identify the drawing act. Other works retrieve similar graffitis in a reference database [11] by using connected components and keypoint matching in an attempt to associate graffiti with gangs.…”

Section: Related Workmentioning

confidence: 99%

Quantifying the Presence of Graffiti in Urban Environments

Tokuda

César

Silva

2019

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)

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Graffiti is a common phenomenon in urban scenarios. Differently from urban art, graffiti tagging is a vandalism act and many local governments are putting great effort to combat it. The graffiti map of a region can be a very useful resource because it may allow one to potentially combat vandalism in locations with high level of graffiti and also to cleanup saturated regions to discourage future acts. There is currently no automatic way of obtaining a graffiti map of a region and it is obtained by manual inspection by the police or by popular participation. In this sense, we describe an ongoing work where we propose an automatic way of obtaining a graffiti map of a neighbourhood. It consists of the systematic collection of street view images followed by the identification of graffiti tags in the collected dataset and finally, in the calculation of the proposed graffiti level of that location. We validate the proposed method by evaluating the geographical distribution of graffiti in a city known to have high concentration of graffiti -São Paulo, Brazil.

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Section: Related Workmentioning

confidence: 99%

Quantifying the Presence of Graffiti in Urban Environments

Tokuda

César

Silva

2019

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)

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“…Similarly, Grassi et al [8] fuse TOF and IR images to detect pedestrians and to classify them according to their moving direction and relative speed. Tombari et al [9] detect graffiti by looking for stationary changes of brightness that do not correspond to depth changes. Image/video segmentation: In Bleiweiss et al [10], state that the fusion of depth and color images results in significant improvements in segmentation of challenging sequences.…”

Section: Related Work: Time-of-flight Based Video Analysismentioning

confidence: 99%

A multi-modal video analysis approach for car park fire detection

Verstockt

Hoecke

Beji

et al. 2013

Fire Safety Journal

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Cataloged from PDF version of article.In this paper a novel multi-modal flame and smoke detector is proposed for the detection of fire in large open spaces such as car parks. The flame detector is based on the visual and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by visual flame feature analysis and amplitude disorder detection. In order to detect the low-cost flame related features, moving objects in visual images are analyzed over time. If an object possesses high probability for each of the flame characteristics, it is labeled as candidate flame region. Simultaneously, the amplitude disorder is also investigated. Also labeled as candidate flame regions are regions with high accumulative amplitude differences and high values in all detail images of the amplitude image's discrete wavelet transform. Finally, when there is overlap of at least one of the visual and amplitude candidate flame regions, fire alarm is raised. The smoke detector, on the other hand, focuses on global changes in the depth images of the time-of-flight camera, which do not have significant impact on the amplitude images. It was found that this behavior is unique for smoke. Experiments show that the proposed detectors improve the accuracy of fire detection in car parks. The flame detector has an average flame detection rate of 93%, with hardly any false positive detection, and the smoke detection rate of the TOF based smoke detector is 88%. (C) 2012 Elsevier Ltd. All rights reserved

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“…Besides that, illegal graffiti is known to cause a negative impact on the local economy: since general people associate it with dirtiness and insecurity, areas containing a wide presence of illegal graffiti are subject to a decline in consumer demand for products and services (such as restaurants, cafes, shops, houses, bus stops, etc.) [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…To try to control and minimize damage, surveillance systems are often used. However, it is costly and impractical for surveillance personnel to monitor and detect graffiti simultaneously on multiple images and cameras [8,10]. For these reasons, more and more effort has been made to control and facilitate graffiti detection through automatic algorithms [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of Lisbon

2023

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This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set.

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Graffiti Detection Using a Time-Of-Flight Camera

Cited by 9 publications

References 17 publications

Quantifying the Presence of Graffiti in Urban Environments

Quantifying the Presence of Graffiti in Urban Environments

A multi-modal video analysis approach for car park fire detection

Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of Lisbon

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