Szczegóły publikacji
Opis bibliograficzny
Pedestrian detection with high-resolution event camera / Piotr WZOREK, Tomasz KRYJAK // W: Progress in Polish artificial intelligence research 4 [Dokument elektroniczny] / ed. by Adam Wojciechowski, Piotr Lipiński. — Wersja do Windows. — Dane tekstowe. — Łódź : Łódź University of Technology Press, 2023. — (Monografie Politechniki Łódzkiej ; nr 2437). — e-ISBN: 978-83-66741-92-8. — S. 55–60. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: http://repozytorium.p.lodz.pl/bitstream/handle/11652/4773/Pro... [2023-10-02]. — Bibliogr. s. 59–60, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149063 |
|---|---|
| Data dodania do BaDAP | 2023-10-24 |
| DOI | 10.34658/9788366741928.7 |
| Rok publikacji | 2023 |
| Typ publikacji | fragment książki |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Politechnika Łódzka |
| Czasopismo/seria | Monografie Politechniki Łódzkiej |
Abstract
Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image quality caused due to challenging lighting conditions. In addition, the frame rate – typically 30 or 60 frames per second – can be a limiting factor in certain scenarios. Event cameras (DVS – Dynamic Vision Sensor) are a potentially interesting technology to address the above mentioned problems. In this paper, we compare two methods of processing event data by means of deep learning for the task of pedestrian detection. We used a representation in the form of video frames, convolutional neural networks and asynchronous sparse convolutional neural networks. The results obtained illustrate the potential of event cameras and allow the evaluation of the effectiveness and efficiency of the methods used for high-resolution (1280 x 720 pixels) footage.
