Saumya Jaipuria
ABSTRACT
Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running Computer Vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize Deep Neural Networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection on edge devices as an Integer Linear Programming (ILP) problem, and then propose a heuristic to solve it. Our experiments show that it is quite effective in practice.
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Index Terms
- A Framework for Tile Processing on Edge Servers for Roadside Traffic Surveillance
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