RT Conference Proceedings
T1 Real-Time Unsupervised Object Localization on the Edge for Airport Video Surveillance.
A1 Ruiz Barroso, Paula
A1 Castro Payán, Francisco Manuel
A1 Guil-Mata, Nicolás
K1 Telecomunicaciones
K1 Aprendizaje automático (Inteligencia artificial)
AB Object localization is vital in computer vision to solve object detection or classification problems. Typically, this task is performed on expensive GPU devices, but edge computing is gaining importance in real-time applications. In this work, we propose a real-time implementation for unsupervised object localization using a low-power device for airport video surveillance. We automatically find regions of objects in video using a region proposal network (RPN) together with an optical flow region proposal (OFRP) based on optical flow maps between frames. In addition, we study the deployment of our solution on an embedded architecture, i.e. a Jetson AGX Xavier, using simultaneously CPU, GPU and specific hardware accelerators. Also, three different data representations (FP32, FP16 and INT8) are employed for the RPN. Obtained results show that optimizations can improve up to 4.1× energy consumption and 2.2× execution time while maintaining good accuracy with respect to the baseline model.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27230
UL https://hdl.handle.net/10630/27230
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026