MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641097958 A) filed by Sri Eshwar College Of Engineering on August 12, 2026, for Integrated Edge To Cloud Analytics For Automatted Traffic Enforcement: Reversing And Multi Hazard Incident Identification.
Inventors include M. Geetha; S. Sheik Kameer Batcha; R. Raj Thilak; and R. Hariharan.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: Real-time detection and response to complex safety violations, especially reversing cars and multi-vehicle hazard scenarios, are becoming increasingly difficult for urban traffic management systems. Conventional surveillance methods, which depend on centralized video processing and human inspection, frequently add latency that compromises prompt enforcement and puts drivers at greater risk. In order to overcome these constraints, this article suggests an integrated edge-to-cloud analytics system for automated traffic enforcement that uses a distributed, event-driven architecture. In order to reduce latency and bandwidth, AI-enabled cameras and localized microservices at the edge carry out frame sampling, motion sensing, and inference right at the point of capture, transforming unprocessed video into lightweight trajectory metadata. In order to identify complex incidents—such as reversing violations and multi-vehicle conflicts—that single-frame or single-source detection methods usually miss, this metadata is streamed through a Kafka-based pipeline where schema validation and temporal sliding-window analytics correlate events across vehicle, pedestrian, and hazard-specific topics. While misclassified samples are returned back into a continuous model retraining loop to gradually increase detection accuracy, processed streams are then stored across specialized storage systems designed for search, video archival, and time-series analysis. The ensuing insights power automated citation issuing, real-time operator dashboards, and predictive analytics for long-term road safety planning. The suggested approach offers a reusable architectural blueprint for smart-city traffic safety applications and shows a feasible route toward low-latency, high-accuracy automated enforcement by fusing edge intelligence with scalable stream processing and multi-model storage.
Disclaimer: Curated by HT Syndication.
