A Self-Contained Traffic Anomaly Detection System based on Distillation Learning for Resource Constrained Edge Deployment
Road traffic anomalies pose a critical threat to public safety, with fatality risk increasing by approximately 2.6% per minute of delayed medical response. While deep learning-based detection systems have shown promise, prevailing approaches either depend on computationally intensive architectures or cloud-based infere...