Pregled bibliografske jedinice broj: 1219544
Moving Objects Tracking using Motion Vectors with Implementation on a Real ADAS Platform
Moving Objects Tracking using Motion Vectors with Implementation on a Real ADAS Platform // PROCEEDINGS ELMAR-2022 / Muštra, Mario ; Cihlar-Zovko, Branka ; Vuković, Josip (ur.).
Zagreb, 2022. str. 61-66 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1219544 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Moving Objects Tracking using Motion Vectors with
Implementation on a Real ADAS Platform
Autori
Radičević, Valentin ; Vranješ, Mario ; Samardžija, Dragan ; Kovačević Jelena
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
PROCEEDINGS ELMAR-2022
/ Muštra, Mario ; Cihlar-Zovko, Branka ; Vuković, Josip - Zagreb, 2022, 61-66
ISBN
978-1-6654-7002-5
Skup
64th International Symposium ELMAR-2022
Mjesto i datum
Zadar, Hrvatska, 12.09.2022. - 14.09.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Motion vectors ; Motion vectors grouping ; Object detection ; ADAS ; Vision SDK
Sažetak
The basic tasks of Advanced Driver-Assistance Systems (ADASs) are object detection and tracking. Although these tasks can be performed by processing signals from various in-vehicle sensors, the most used is the image captured by the in-vehicle camera. For an autonomous vehicle to know which action needs to be taken at a given moment, very useful data is about the speed and direction of movement of objects in their surroundings. Once objects are detected, different methods can be used to track them. Although there are various methods for object tracking based on computer vision and machine learning, they are very often not suitable for implementation on the real embedded ADAS platforms that have limited memory and computing resources. Therefore, this paper deals with the motion vectors (MVs) estimation between adjacent frames and their grouping with the goal of identifying and tracking objects around the ego-vehicle, all accompanied by the implementation of the proposed solutions onto a real embedded ADAS platform. Different methods for MV estimation and grouping are examined. Special attention was given to the efficient implementation of each task on the ADAS platform. The proposed solution achieves high performance in terms of accuracy when tested on real-life traffic videos. It is important to note that the solution is able to process 64 frames per second for the input images with a resolution of 1280x720 pixels.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Projekti:
DGS-UNIOS-ZUP 2018-6 - Povećavanje razine pouzdanosti vožnje autonomnih vozila pomoću sustava kamera na vozilu (Vranješ, Mario, DGS - Interni natječaja Sveučilišta Josipa Jurja Strossmayera u Osijeku za znanstvenoistraživačke i umjetničke projekte UNIOS-ZUP 2018) ( CroRIS)
MZO Ustanova-Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek-IZIP FERIT 2022 - Prilagodba modela dubokog učenja za primjenu u ADAS algoritmima (Vranješ, Mario, MZO Ustanova ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
Profili:
Mario Vranješ
(autor)
