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Recent advances in computer vision algorithms and video streaming technologies have facilitated the development of edge-server-based video analytics systems, enabling them to process sophisticated real-world tasks, such as traffic surveillance and workspace monitoring. Meanwhile, due to their omnidirectional recording capability, 360-degree cameras have been proposed to replace traditional cameras in video analytics systems to offer enhanced situational awareness. Yet, we found that providing an efficient 360-degree video analytics framework is a non-trivial task. Due to the higher resolution and geometric distortion in 360-degree videos, existing video analytics pipelines fail to meet the performance requirements for end-to-end latency and query accuracy. To address these challenges, we introduce the innovative ST-360 framework specifically designed for 360-degree video analytics. This framework features a spatial-temporal filtering algorithm that optimizes both data transmission and computational workloads. Evaluation of the ST-360 framework on a unique dataset of 360-degree first-responders videos reveals that it yields accurate query results with a 50% reduction in end-to-end latency compared to state-of-the-art methods.