Integrating location tracking, traffic monitoring and semantics in a layered ITS architecture

Faro, Alberto; Giordano, Daniela; Spampinato, Concetto

doi:10.1049/iet-its.2010.0141

Cited by 38 publications

(15 citation statements)

References 8 publications

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“…This comparative analysis shows that specific algorithms perform better under these conditions. These algorithms model the background with either (i) a mixture of probability density function (PDF) models [34,36,37]; (ii) a frequency transform to catch temporal color variation of background pixels [38]; or (iii) intrinsic images [39] given as the temporal median of the frames' reflectance component (which is assumed to be light invariant).…”

Section: Fish Detectionmentioning

confidence: 99%

A rule-based event detection system for real-life underwater domain

Spampinato

Beauxis-Aussalet

Palazzo

et al. 2013

Machine Vision and Applications

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Understanding and analyzing fish behaviour is a fundamental task for biologists that study marine ecosystems because the changes in animal behaviour reflect environmental conditions such as pollution and climate change. To support investigators in addressing these complex questions, underwater cameras have been recently used. They can continuously monitor marine life while having almost no influence on the environment under observation, which is not the case with observations made by divers for instance. However, the huge quantity of recorded data make the manual video analysis practically impossible. Thus machine vision approaches are needed to distill the information to be investigated. In this paper, we propose an automatic event detection system able to identify solitary and pairing behaviours of the most common fish species of the Taiwanese coral reef. More specifically, the proposed system employs robust low-level processing modules for fish detection, tracking and recognition that extract the raw data used in the event detection process. Then each fish trajectory is modeled and classified using hidden Markov models. The events of interest are detected by integrating end-user rules, specified through an ad hoc user interface, and the analysis of fish trajectories. The system was tested on 499 events of interest, divided into solitary and pairing events for each fish species. It achieved an average accuracy of 0.105, expressed in terms of normalized detection cost. The obtained results are promising, especially given the difficulties occurring in underwater environments. And moreover, it allows marine biologists to speed up the behaviour analysis process, and to reliably carry on their investigations.

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Section: Fish Detectionmentioning

confidence: 99%

A rule-based event detection system for real-life underwater domain

Spampinato

Beauxis-Aussalet

Palazzo

et al. 2013

Machine Vision and Applications

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show abstract

“…Porikli in [44] have compared different algorithms (both for detection and tracking) under extreme conditions that somehow recall the ones present in underwater scenes such as erratic motion, sudden and global light change, presence of periodic and multimodal background, arbitrary changes in the observed scene, low contrast and noise. In detail, the authors have shown that the algorithms that best perform under these conditions use mixture of probability density function ( pdf ) models [23,24,62], Wave-Back Model [45] and Intrinsic Model [43]. In detail, mixture of pdf models have been adopted to handle multimodal backgrounds.…”

Section: Object Detection Ad Tracking Under Extreme Conditionsmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

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“…This motivates why it has been proposed to extend the LBSs with a semantic layer able to provide the mobile users with the real time and off line information coming from the multitude of databases relevant for mobile activities, hopefully by using the same mobile device [1].…”

Section: Introductionmentioning

confidence: 99%

Location based services for mobile users: a scenario based implementation account

Faro¹,

Spampinato²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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-Many Location Based Services (LBSs) have been proposed, but they are developed mainly with a proprietary approach, i.e., they are specialized for managing specific tasks and are not able to make available the collected information to the services offered by other providers, thus limiting the potential benefits for the users. For this reason LBSs should be extended with a semantic layer able to provide the mobile users with the real time and off line information coming from the multitude of databases relevant for LBS activities, hopefully by using the same mobile device. Aim of the paper is to show how this can be accomplished in practice by integrating three methodologies in a single web architecture: a) a scenario based design methodology to implement the use stories of the mobile users, b) a semantic data management methodology to allow data integration, and c) a suitable JQuery Mobile based interface to allow the RoR web server to inform people on the available services independently on the adopted mobile.

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Integrating location tracking, traffic monitoring and semantics in a layered ITS architecture

Cited by 38 publications

References 8 publications

A rule-based event detection system for real-life underwater domain

A rule-based event detection system for real-life underwater domain

Understanding fish behavior during typhoon events in real-life underwater environments

Location based services for mobile users: a scenario based implementation account

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