DeVA: An Edge-Assisted Video Analytics Framework for Depth Estimation

Edge-assisted video analytics frameworks, which offload vision-based tasks to edge servers, offer a promising approach to enhance accuracy while minimizing network resource overhead. However, these frameworks often overlook depth estimation, a critical task for applications like augmented reality and intelligent surveillance. Depth estimation, which calculates the distance between objects and the camera, generates depth images with unique characteristics, making existing approaches impractical or inefficient for video analytics in this context. In this work, we present DeVA, an edge-assisted video analytics framework for depth estimation that ensures accuracy with minimal network resource overhead. We examine the impact of various video analytics configurations, including resolution and quantization parameter (QP), on accuracy. Additionally, we analyze the region of interest (RoI) for depth estimation and propose methods for tracking RoI areas locally on the device. DeVA features an adaptive video encoding mechanism that dynamically adjusts the resolution for offloaded video and optimizes QPs for RoI and non-RoI areas. We implement DeVA and evaluate its performance using public video datasets. The results show that DeVA reduces 57.12% of the bandwidth overhead while keeping depth estimation errors within acceptable limits, demonstrating a great balance between accuracy and network resource usage.