We propose a novel method of improving algorithms recognizing traffic lights in video sequences. Our focus is on algorithms for applications which notify the driver of a light in sight. Many existing methods process images in the recording separately. Our method bases on the observation that real-life videos depict underlying continuous processes. We named our method FSA (Frame Sequence Analyzed). It is applicable for any underlying algorithm and improves it by adding an additional result post-processing step. Our experiments are based on improving a published realtime traffic light recognition algorithm. Its general description has been provided by its authors, which allowed us to create a best-effort implementation for testing. We verify the effectiveness of the FSA method on a public dataset, acquiring very good results - improving the underlying algorithm in terms of all considered error measures. In the end, conclusions and possible future improvements are discussed.
Autorzy
- Dr inż Adam Blokus,
- prof. dr hab. inż. Henryk Krawczyk link otwiera się w nowej karcie
Informacje dodatkowe
- DOI
- Cyfrowy identyfikator dokumentu elektronicznego link otwiera się w nowej karcie 10.1109/iwssip.2018.8439187
- Kategoria
- Aktywność konferencyjna
- Typ
- materiały konferencyjne indeksowane w Web of Science
- Język
- angielski
- Rok wydania
- 2018
Źródło danych: MOSTWiedzy.pl - publikacja "Improving Traffic Light Recognition Methods using Shifting Time-Windows" link otwiera się w nowej karcie