Efficient Edge Video Analytics with Region-Aware Enhancement and Temporal Consistency
Jisoo Kang
Faculty of Engineering
Pierre Faure
Built Environment and Information Technology
Youngjae Bang
University of Pretoria, Pretoria
Keywords: Edge Computing, Video Analytics, Region-Aware Processing, Temporal Consistency
Abstract
The exponential proliferation of connected vision sensors has fundamentally transformed the landscape of automated surveillance, intelligent transportation systems, and industrial monitoring. Conventional paradigms that rely on transmitting continuous, high-definition video streams to centralized cloud architectures are increasingly untenable due to severe bandwidth constraints, inherent transmission latency, and profound privacy concerns. Edge computing has emerged as a compelling alternative by migrating computational resources closer to the data source. However, edge devices frequently possess constrained computational capabilities and limited thermal budgets, rendering the execution of complex deep neural networks highly challenging. This research presents a comprehensive framework for efficient edge video analytics characterized by two novel components. First, we introduce a region-aware enhancement mechanism that selectively allocates computational resources to spatial areas of high analytical value, thereby discarding irrelevant background information and significantly reducing spatial redundancy. Second, we integrate a temporal consistency module designed to leverage the inherent continuity across sequential frames. By propagating high-level semantic features from previous frames to current frames using lightweight motion estimation, the system minimizes redundant computations while ensuring smooth and stable analytical outputs. Through extensive evaluation on standard video analytic datasets, our proposed methodology demonstrates substantial improvements in processing speed and bandwidth utilization without compromising analytical accuracy.
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