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The Centre of Research Excellence for Advanced Cooperative Systems (ACROSS) is an interdepartmental research centre at the University of Zagreb Faculty of Electrical Engineering and Computing. The centre performs research in cooperative systems related to robotics, networked embedded systems and renewable energy systems. Its establishment and operation is funded by the European FP-7 Capacities "Research Potential" program [285939, FP7-REGPOT-2011-1].

Published: 2014-03-13 at 08:55
Colloquium "Addressing visual...

The Centre of Research Excellence for Advanced Cooperative Systems (ACROSS) invites you to the colloquium

"Addressing visual similarity and standardization as traffic signs recognition problems"

held by Ivan Filković, mag. ing. comp.

Colloquium details
Title Addressing visual similarity and standardization as traffic signs recognition problems
Speakers Ivan Filković, mag. ing. comp.
Date 14. 3. 2014. 14:15 - 15:00
Location Faculty of Electrical Engineering and Computing, TCR

More about the speaker and colloquium can be found in the detailed news content.

In Advanced Driver Assistance Systems (ADAS), which are intended to assist the driver in the driving process, there is a need for the designing of a system for the detection, tracking and recognition of traffic signs in the vicinity of the vehicle. The goal is to maximize the safety of the passengers inside the vehicle and consequently all other traffic participants. Also, pipeline which consist of traffic sign detection, recognition and tracking has application in autonomous intelligent vehicles and in mapping and assessing the state of traffic infrastructure. The aim of detecting traffic signs, within frames in video sequences, is to determine regions of interest where there is a potential candidate of traffic sign. Another point of view on traffic sign detection problem is as a segmentation problem where there are two classes: traffic sign and background class. Traffic sign recognition methods classify detected traffic sign candidates in specific predetermined classes or super-classes. To improve overall detection and recognition accuracy of traffic signs in the scene, traffic sign instances (real world signs) tracking  between adjacent frames in video sequences is conducted. Publicly available traffic signs datasets are described. System for detection, tracking and recognition of traffic signs and a system for evaluating traffic signs recognition methods are presented. The obtained results were analyzed in terms of recognition accuracy and computational complexity. Problems that occur during traffic sign recognition are presented. Overall conclusion and future goals are given.



Ivan Filković has studied Computer Science at the University of Zagreb, Faculty of Electrical Engineering and Computing where he received his Bachelor's degree (BSc; 2011) and Master of Science degree (MSc; 2013). While studying he focused on the field of Computer Vision and because of that topics of his thesis were from that area. The subject of Bachelor's thesis was  "Object tracking based on adaptive search-window shift" and the subject of Master's thesis was "Evaluation of procedures for traffic signs recognition". The work on thesis was mentored by prof. dr. sc. Zoran Kalafatić. Presently he is studying at the same faculty on postgraduate doctoral study programme towards the Ph.D. academic degree in the scientific field of Computing. Currently he is employed as a research assistant on project VISTA where he's working on a system for the detection and recognition of traffic signs and its application in advanced driver assistance systems - ADAS. The goal of traffic sign detection in video sequences is accurate localization of the traffic sign and region of interest assessment where there is a potential traffic sign candidate. Procedures for traffic signs recognition assigns detected traffic signs to specific classes or categories. Previously he worked in Institute for Nuclear Technology (INETEC) on secondary verification system for positioning the manipulator based on Computer Vision methods.

Mario Bukal
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ACROSS project has received research funding from the Seventh Framework Programme of the European Union (Grant Agreement No. 285939 FP7-REGPOT-2011-1).


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