BlockHybrid: Accelerating Object Detection Pipelines With Hybrid Block-Wise Execution

Latency-sensitive edge video analytics applications require rapid responses for real-time decision-making, driving the demand for efficient object detection pipelines. Conventional pipelines transmit and process full frames, overlooking redundancy in videos and leading to unnecessary resource consumption. Existing block-wise conditional execution methods mitigate this issue by processing only informative blocks. However, they treat all informative blocks equally and fail to further categorize these blocks. To address this limitation, we propose BlockHybrid, an edge video analytics framework designed to accelerate object detection pipelines by hybrid block-wise execution. Specifically, BlockHybrid classifies blocks into hard or easy blocks using a policy network. Hard blocks are transmitted and processed by a block-wise detector on the server, while easy blocks are handled by an efficient tracker locally on the camera, reducing redundant computation and communication. Extensive experiments demonstrate that BlockHybrid can achieve 8.8%–31.5% higher local execution speed and comparable detection accuracy compared to state-of-the-art methods, and accelerate end-to-end processing—including camera-to-server communication—by 31.5%–39.1% in a real-world testbed.