RT info:eu-repo/semantics/article
T1 A self-adaptive automatic incident detection system for road surveillance based on deep learning
A1 Bartolomé Hornillos, César
A1 San José Revuelta, Luis Miguel
A1 Aguiar Pérez, Javier Manuel
A1 García Serrada, Carlos
A1 Vara Pazos, Eduardo
A1 Casaseca de la Higuera, Juan Pablo
K1 Security
K1 Systems and Data Security
K1 Artificial intelligence
K1 Traffic safety
K1 Seguridad en carretera
K1 Automobiles - Safety appliances
K1 Seguridad vial
K1 Tráfico, Ingeniería del - Informática
K1 Highway communications
K1 Machine learning
K1 Aprendizaje automático
K1 Self-adaptive software
K1 Video surveillance
K1 Video
K1 Artificial intelligence
K1 1203.04 Inteligencia Artificial
K1 1203.25 Diseño de Sistemas Sensores
AB We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera’s field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2–5% in automatic mode and 2–7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices.
PB MDPI
SN 1424-8220
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/68101
UL https://uvadoc.uva.es/handle/10324/68101
LA eng
NO Sensors, 2024, Vol. 24, Nº. 6, 1822
NO Producción Científica
DS UVaDOC
RD 27-jun-2024