TY - GEN
T1 - Object Detection in Movies – Case Study
AU - Nurhasan, Amnaduny Akhara
AU - Kasprowski, Pawel
AU - Harezlak, Katarzyna
AU - Birawo, Birtukan Adamu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This work analyzed video datasets distinctively characterized by varying properties such as duration, fps, and total frames. These datasets also displayed differences in lighting intensities, image shifting, and rotations. The study utilized the YOLOv4 architecture integrated with the Darknet framework for object detection purposes. This combination efficiently extracted bounding box coordinates, defining the Region of Interest (ROI) of the Primary Display Panel in the plane simulator cockpit. Within this ROI, the research employed the SIFT and FAST algorithms, with the Manhattan distance, to determine corner points. The results revealed the FAST algorithm’s ability to detect more key points quicker than SIFT. On the other hand, the latter one turned out to be slightly better when the average Intersection over Union (IoU) is considered. The study’s outcomes can serve for future analysis of pilots’ behavior.
AB - This work analyzed video datasets distinctively characterized by varying properties such as duration, fps, and total frames. These datasets also displayed differences in lighting intensities, image shifting, and rotations. The study utilized the YOLOv4 architecture integrated with the Darknet framework for object detection purposes. This combination efficiently extracted bounding box coordinates, defining the Region of Interest (ROI) of the Primary Display Panel in the plane simulator cockpit. Within this ROI, the research employed the SIFT and FAST algorithms, with the Manhattan distance, to determine corner points. The results revealed the FAST algorithm’s ability to detect more key points quicker than SIFT. On the other hand, the latter one turned out to be slightly better when the average Intersection over Union (IoU) is considered. The study’s outcomes can serve for future analysis of pilots’ behavior.
KW - FAST
KW - Object Detection
KW - SIFT
KW - YOLO
UR - https://www.scopus.com/pages/publications/85202177119
U2 - 10.1007/978-981-97-5934-7_1
DO - 10.1007/978-981-97-5934-7_1
M3 - Conference contribution
AN - SCOPUS:85202177119
SN - 9789819759330
T3 - Communications in Computer and Information Science
SP - 3
EP - 12
BT - Recent Challenges in Intelligent Information and Database Systems - 16th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2024, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Wojtkiewicz, Krystian
A2 - Chbeir, Richard
A2 - Manolopoulos, Yannis
A2 - Fujita, Hamido
A2 - Hong, Tzung-Pei
A2 - Nguyen, Le Minh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th Asian Conference on Intelligent Information and Database Systems , ACIIDS 2024
Y2 - 15 April 2024 through 18 April 2024
ER -