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.

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.