RT Journal Article
T1 Real-time unsupervised video object detection on the edge
A1 Ruiz Barroso, Paula
A1 Castro Payán, Francisco Manuel
A1 Guil-Mata, Nicolás
K1 Telecomunicaciones
K1 Aprendizaje automático (Inteligencia artificial)
K1 Detección
AB Object detection in video is an essential computer vision task. Consequently, many efforts have been devoted to developing precise and fast deep-learning models for this task. These models are commonly deployed on discrete and powerful GPU devices to meet both frame rate performance and detection accuracy requirements. Furthermore, model training is usually performed in a strongly supervised way so that samples must be previously labelled by humans using a slow and costly process. In this paper, we develop a real-time implementation for unsupervised object detection in video employing a low-power device. We improve typical approaches for object detection using information supplied by optical flow to detect moving objects. Besides, we use an unsupervised clustering algorithm to group similar detections that avoid manual object labelling. Finally, we propose a methodology to optimize the deployment of our resulting framework on an embedded heterogeneous platform. Thus, we illustrate how all the computational resources of a Jetson AGX Xavier (CPU, GPU, and DLAs) can be used to fulfil frame rate, accuracy, and energy consumption requirements. Three different data representations (FP32, FP16 and INT8) are studied for the pipeline networks in order to evaluate the impact of all of them in our pipeline. Obtained results show that our proposed optimizations can improve up to 23.6x energy consumption and  32.2x execution time with respect to the non-optimized pipeline without penalizing the original mAP (59.44). This computational complexity reduction is achieved through knowledge distillation, using FP16 data precision, and deploying concurrent tasks in different computing units.
PB Elsevier
SN 0167-739X
YR 2025
FD 2025-02-06
LK https://hdl.handle.net/10630/38549
UL https://hdl.handle.net/10630/38549
LA eng
NO Ruiz-Barroso, P., Castro, F. M., & Guil, N. (2025). Real-time unsupervised video object detection on the edge. Future Generation Computer Systems, 167, 107737.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026