ALBIREO (SEC 2026)
2026
- SECAmir Taherin, José Cano, Bin Ren, and 2 more authorsIn ACM/IEEE Symposium on Edge Computing (SEC), 2026. To appear.
Video object detection on edge devices requires running computationally expensive detectors over long frame streams, leading to high energy consumption and sustained GPU utilization. Although consecutive video frames contain substantial temporal redundancy, naive frame-skipping strategies are content-blind: they skip during critical moments such as object entry, occlusion recovery, and abrupt motion, causing substantial detection-quality loss. We present ALBIREO, a detector-agnostic, codec-free, adaptive inference framework that wraps off-the-shelf object detectors and decides when detector invocation can be safely skipped based on scene content and per-object temporal state. ALBIREO requires no detector modification or detector retraining and is designed as a drop-in efficiency layer for improving the accuracy-energy-latency tradeoff of edge video-detection pipelines. ALBIREO maintains a 10-dimensional Kalman filter (KF) for each active object state and invokes the detector only when prediction uncertainty exceeds a threshold. On skipped frames, bounding boxes are predicted from the KF state at near-zero GPU cost. A KF-based rescue mechanism preserves confirmed object states through brief detector misses, and a lightweight empty-scene screen further avoids full detector calls on objectless frames. We evaluate ALBIREO on the BDD100K MOT validation split using three architecturally distinct detectors (YOLO11x, YOLO26x, and RF-DETR-Large) on two NVIDIA Jetson platforms (AGX Thor and AGX Orin). Across all detector-platform configurations, ALBIREO keeps AP@50 within ±1.2 pp of per-frame inference while reducing total energy by 12.1-17.6%. On the primary YOLO26x configuration, ALBIREO improves AP@50 by +0.8 pp while reducing energy by 17.6% on Thor and 14.4% on Orin, and reduces per-frame energy-delay product by 24.9% on Thor and 26.1% on Orin.
@inproceedings{taherin2026albireo, title = {ALBIREO: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge}, author = {Taherin, Amir and Cano, José and Ren, Bin and Wang, Yanzhi and Kaeli, David}, booktitle = {ACM/IEEE Symposium on Edge Computing (SEC)}, year = {2026}, }