Year: 2024
Type: conference-paper
Abstract: The proliferation of video analytics in applications like autonomous driving, traffic surveillance, and teleoperated vehicles requires on-premise (on edge) execution of deep learning models to meet latency requirements and curb bandwidth usage by limiting frequent offloading of inference tasks. However, constrained by the compute and power availability on the edge, a cheaper model is typically deployed. These shallower models have two major associated problems: 1) using the same model for all cameras/vehicles gives inconsistent accuracy, and 2) trained models are prone to data drift.
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405014600