Despite many years of active research traffic sign detection and recognition is still an open problem. Recognition accuracy and false detections rate are still not sufficient for replacing human operators with automatic recognition system. We are working on several topics in this area:

• high-speed detection
• sign recognition with multi-layer neural networks
• synthetic datasets generation for training

Datasets

• Synthetic traffic signs dataset:
  • Dataset
  • md5 checksum
  • .bib file for reference .

Team

• Anton S. Konushin, Lomonosov Moscow State University
• Alexander Chigorin, Lomonosov Moscow State Univesity
• Boris D. Moiseev, Lomonosov Moscow State University
• Vladislav Shakhuro, Lomonosov Moscow State University
• Maxim Novikov, Lomonosov Moscow State University

Publications

• Chigorin A, Konushin A. A system for large-scale automatic traffic sign recognition and mapping. CMRT13 ? City Models, Roads and Traffic 2013 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. II-3/W3), 2013 (download)
• Moiseyev B, Konev A, Chigorin A, Konushin A. Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data. Advanced Concepts for Intelligent Vision Systems (Springer LNCS, Vol. 8192), 2013 (download)
• Chigorin A, Krivovyaz G, Velizhev A, Konushin A. A method for traffic sign detection in an image with learning from synthetic data. 14th International Conference Digital Signal Processing and its Applications, 2012 (download)
• ×èãîðèí , Êîíåâ , Êðèâîâÿçü , Âåëèæåâ , Êîíóøèí À. Ðàñïîçíàâàíèå çíàêîâ äîðîæíîãî äâèæåíèÿ íà èçîáðàæåíèÿõ ñ îáó÷åíèåì íà ñèíòåòè÷åñêèõ äàííûõ. Òåõíè÷åñêîå çðåíèå â ñèñòåìàõ óïðàâëåíèÿ, 2012 (download)
• Êîíóøèí À, ×èãîðèí . Ñèñòåìà àâòîìàòè÷åñêîãî êàðòîãðàôèðîâàíèÿ çíàêîâ äîðîæíîãî äâèæåíèÿ. Ïðîãðàììíûå ïðîäóêòû è ñèñòåìû, 2013 (download)
• ×èãîðèí , Êîíóøèí À. Ýêñïåðèìåíòû ñ îáó÷åíèåì ìåòîäîâ ðàñïîçíàâàíèÿ äîðîæíûõ çíàêîâ íà ñèíòåòè÷åñêèõ äàííûõ. ÍÀÓÊÀ È ÎÁÐÀÇÎÂÀÍÈÅ: ÝËÅÊÒÐÎÍÍÎÅ ÍÀÓ×ÍÎ-ÒÅÕÍÈ×ÅÑÊÎÅ ÈÇÄÀÍÈÅ, 2013 (download)

Acknowledgements

This project is partially supported by grant RFBR 12-01-33085 "Learning probabilistic models for image recognition, scanning data processing, computer vision".