2019 |
Wilkowski, Artur; Mykhalevych, Ihor; Luckner, Marcin City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms Inproceedings Advances in Intelligent Systems and Computing, pp. 326–336, 2019, ISSN: 21945357. Abstract | Links | BibTeX | Tagi: Computer vision, Detection, Tracking, Traffic monitoring @inproceedings{Wilkowski2019, title = {City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms}, author = {Artur Wilkowski and Ihor Mykhalevych and Marcin Luckner}, doi = {10.1007/978-3-030-13273-6_31}, issn = {21945357}, year = {2019}, date = {2019-01-01}, booktitle = {Advances in Intelligent Systems and Computing}, volume = {920}, pages = {326--336}, abstract = {In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.}, keywords = {Computer vision, Detection, Tracking, Traffic monitoring}, pubstate = {published}, tppubtype = {inproceedings} } In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections. |
Wilkowski, Artur; Mykhalevych, Ihor; Luckner, Marcin City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms Inproceedings Advances in Intelligent Systems and Computing, pp. 326–336, 2019, ISSN: 21945357. Abstract | Links | BibTeX | Tagi: Computer vision, Detection, Tracking, Traffic monitoring @inproceedings{Wilkowski2019b, title = {City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms}, author = {Artur Wilkowski and Ihor Mykhalevych and Marcin Luckner}, doi = {10.1007/978-3-030-13273-6_31}, issn = {21945357}, year = {2019}, date = {2019-01-01}, booktitle = {Advances in Intelligent Systems and Computing}, volume = {920}, pages = {326--336}, abstract = {In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.}, keywords = {Computer vision, Detection, Tracking, Traffic monitoring}, pubstate = {published}, tppubtype = {inproceedings} } In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections. |
2013 |
Luckner, Marcin; Filasiak, Robert Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. Abstract | Links | BibTeX | Tagi: Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS) @inproceedings{Luckner2013b, title = {Flow-level Spam Modelling using separate data sources}, author = {Marcin Luckner and Robert Filasiak}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6643981}, year = {2013}, date = {2013-01-01}, booktitle = {Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on}, pages = {91 -- 98}, publisher = {IEEE}, abstract = {Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses.}, keywords = {Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS)}, pubstate = {published}, tppubtype = {inproceedings} } Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses. |
Luckner, Marcin; Filasiak, Robert Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. Abstract | Links | BibTeX | Tagi: Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS) @inproceedings{Luckner2013bb, title = {Flow-level Spam Modelling using separate data sources}, author = {Marcin Luckner and Robert Filasiak}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6643981}, year = {2013}, date = {2013-01-01}, booktitle = {Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on}, pages = {91 -- 98}, publisher = {IEEE}, abstract = {Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses.}, keywords = {Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS)}, pubstate = {published}, tppubtype = {inproceedings} } Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses. |
Publikacje
2019 |
City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms Inproceedings Advances in Intelligent Systems and Computing, pp. 326–336, 2019, ISSN: 21945357. |
City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms Inproceedings Advances in Intelligent Systems and Computing, pp. 326–336, 2019, ISSN: 21945357. |
2013 |
Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. |
Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. |