Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge

arXiv:2609.29648 · cs.CV, cs.AR, cs.LG, cs.PF, cs.RO · Submitted 2026-08-29 · Read on arXiv

cs.CV, cs.AR, cs.LG, cs.PF, cs.RO

Submitted: 2026-08-29

Updated: 2026-08-29

Code: https://github.com/amirtaherin/albireo

Terminology

• Accuracy — 29× in this paper · explained in 1 episode
• Aggregation — 7× in this paper · explained in 1 episode
• Redundancy — 6× in this paper · explained in 2 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Energy Efficiency — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 4× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Edge Deployment — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Deep Feature Flow — 2× in this paper
• Distillation — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• IEEE International Conference on Image Processing — 2× in this paper
• International Conference on Computer Vision — 2× in this paper
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Confidence-Triggered Detection — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• European Conference on Computer Vision — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• IEEE Intelligent Vehicles Symposium — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
• YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection
• Practical Video Object Detection via Feature Selection and Aggregation
• Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems
• See Without Decoding: Motion-Vector-Based Tracking in Compressed Video
• ComPrivDet: Efficient Privacy Object Detection in Compressed Domains Through Inference Reuse
• YOLOv11: An Overview of the Key Architectural Enhancements
• Seq-NMS for Video Object Detection
• Real-time Online Multi-Object Tracking in Compressed Domain

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models