101 research outputs found
Traversing the Labyrinth: A Comprehensive Analysis of Pedestrian Traffic in Venice
The purpose of this project was to contribute to the development of a computer model to assist the City of Venice in the management of pedestrian traffic congestion. In collaboration with the Santa Fe Complex, the team confirmed the feasibility of the model by producing a prototype that effectively simulates pedestrian mobility in western San Marco. Additionally, the team determined that the existing networks of surveillance cameras could be leveraged to automatically feed the model in future years
A distributed vision system for boat traffic monitoring in the venice grand canal
Motion detection and Tracking, Distribuited surveillance, Boat traffic monitoring In this paper we describe a system for boat traffic monitoring that has been realized for analyzing and computing statistics of trafic in the Grand Canal in Venice. The system is based on a set of survey cells to monitor about 6 Km of canal. Each survey cell contains three cameras oriented in three directions and covering about 250-300 meters of the canal. This paper presents the segmentation and tracking phases that are used to detect and track boats in the channel and experimental evaluation of the system showing the effectiveness of the approach in the required tasks.
Enhancing automatic maritime surveillance systems with visual information
Automatic surveillance systems for the maritime
domain are becoming more and more important due to a constant
increase of naval traffic and to the simultaneous reduction of
crews on decks. However, available technology still provides only
a limited support to this kind of applications. In this paper,
a modular system for intelligent maritime surveillance, capable
of fusing information from heterogeneous sources, is described.
The system is designed to enhance the functions of the existing
Vessel Traffic Services systems and to be deployable in populated
areas, where radar-based systems cannot be used due to the high
electromagnetic radiation emissions. A quantitative evaluation
of the proposed approach has been carried out on a large
and publicly available data set of images and videos, collected
from multiple real sites, with different light, weather, and traffic
conditions
Boats and Wakes
This group aimed to quantify levels of boat wakes in the Venetian canals over the past decade. The group found that both boat traffic and boat wakes are increasing, and will continue to increase. The group then examined several ways Venice could reduce these destructive boat wakes. The four possibilities include; changing the hull shape, enforcing speed limits, a taxi re-engineering plan, and a cargo re-engineering plan. If adapted, these proposals could reduce boat wakes by 57% in Venetian Canals
An intelligent surveillance platform for large metropolitan areas with dense sensor deployment
ProducciĂłn CientĂficaThis paper presents an intelligent surveillance platform based on the usage of
large numbers of inexpensive sensors designed and developed inside the European Eureka
Celtic project HuSIMS. With the aim of maximizing the number of deployable units while
keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is
based on the usage of inexpensive visual sensors which apply efficient motion detection
and tracking algorithms to transform the video signal in a set of motion parameters. In
order to automate the analysis of the myriad of data streams generated by the visual
sensors, the platform’s control center includes an alarm detection engine which comprises
three components applying three different Artificial Intelligence strategies in parallel.
These strategies are generic, domain-independent approaches which are able to operate in
several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The
architecture is completed with a versatile communication network which facilitates data
collection from the visual sensors and alarm and video stream distribution towards the
emergency teams. The resulting surveillance system is extremely suitable for its
deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap
visual sensors and autonomous alarm detection facilitate dense sensor network deployments
for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti
A survey of potential users of the High Altitude Powered Platform (HAPP) in the ocean/coastal zone community
The results of a survey of the ocean/coastal zone community to determine potential applications of a High Altitude Powered Platform (HAPP) are reported. Such a platform, capable of stationkeeping for periods up to a year over a given location, could make frequent and repeated high resolution observations over a given region or serve as a high-altitude regional communications link. Users were surveyed in person and via a questionnaire to determine the desirability of the HAPP within the ocean/coastal zone community. The results of the survey indicated that there is strong interest in all areas of the user community (research and development, operational agencies, and private industry) in having NASA develop the HAPP
Modified inter prediction H.264 video encoding for maritime surveillance
Video compression has evolved since it is first being standardized. The most popular CODEC, H.264 can compress video effectively according to the quality that is required. This is due to the motion estimation (ME) process that has impressive features like variable block sizes varying from 4Ă—4 to 16Ă—16 and quarter pixel motion compensation. However, the disadvantage of H.264 is that, it is very complex and impractical for hardware implementation. Many efforts have been made to produce low complexity encoding by compromising on the bitrate and decoded quality. Two notable methods are Fast Search Mode and Early Termination. In Early Termination concept, the encoder does not have to perform ME on every macroblock for every block size. If certain criteria are reached, the process could be terminated and the Mode Decision could select the best block size much faster. This project proposes on using background subtraction to maximize the Early Termination process. When recording using static camera, the background remains the same for a long period of time where most macroblocks will produce minimum residual. Thus in this thesis, the ME process for the background macroblock is terminated much earlier using the maximum 16Ă—16 macroblock size. The accuracy of the background segmentation for maritime surveillance video case study is 88.43% and the true foreground rate is at 41.74%. The proposed encoder manages to reduce 73.5% of the encoding time and 80.5% of the encoder complexity. The bitrate of the output is also reduced, in the range of 20%, compared to the H.264 baseline encoder. The results show that the proposed method achieves the objectives of improving the compression rate and the encoding time
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