Year: 2025
Type: article
Abstract: It is critical to solidify surveillance in 3D environments with heterogeneous sensors. This study introduces an innovative deep learning-assisted surveillance reinforcement system with smart sensors for resource-constrained cyber-physical devices and mobile elements. The proposed system incorporates deep learning technologies to address the challenges of dynamic public environments. By enhancing the adaptability and effectiveness of surveillance in environments with high human mobility, this paper aims to optimize surveillance node placement and ensure real-time system responsiveness. The integration of deep learning not only improves accuracy and efficiency but also introduces unprecedented flexibility in surveillance operations.
Source: Sensors
DOI: 10.3390/s25113345
Authors: Taewoo Lee, Yumin Choi, Hyunbum Kim
Institution: Incheon National University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410784267