Real-Time Visual Anomaly Detection in High-Speed Motorsport: An Entropy-Driven Hybrid Retrieval- and Cache-Augmented Architecture
Rubén Juárez Cádiz, Fernando Rodríguez-Sela
TL;DR
This paper introduces a real-time system for detecting visual anomalies in high-speed motorsport using a hybrid architecture that balances speed and accuracy.
Contribution
The novel hybrid cache–retrieval architecture reduces latency while maintaining high detection accuracy in real-time motorsport monitoring.
Findings
The system achieves 55.3% lower latency compared to a retrieval-only baseline.
It attains a Macro-F1 score of 0.89 for detecting anomalies like tire degradation and suspension issues.
The framework uses entropy-based routing to stabilize decisions under uncertainty.
Abstract
At 300 km/h, an end-to-end vision delay of 100 ms corresponds to 8.3 m of unobserved travel; therefore, real-time anomaly monitoring must balance sensitivity with strict tail-latency constraints at the edge. We propose a hybrid cache–retrieval inference architecture for visual anomaly detection in high-speed motorsport that exploits lap-to-lap spatiotemporal redundancy while reserving local similarity retrieval for genuinely uncertain events. The system combines a hierarchical visual encoder (a lightweight backbone with selective refinement via a Nested U-Net for texture-level cues) and an uncertainty-driven router that selects between two memory pathways: (i) a static cache of precomputed scene embeddings for track/background context and (ii) local similarity retrieval over historical telemetry–vision patterns to ground ambiguous frames, improve interpretability, and stabilize…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Autonomous Vehicle Technology and Safety · Human Pose and Action Recognition