Development Of Energy-Efficient And Secure Cryptographic Protocols Leveraging Elliptic Curve And Block Cipher Techniques For Real-Time Iot Network Applications
DOI:
https://doi.org/10.29284/6ndt0n63Keywords:
Optical Flow, Motion Heat Maps, Harris Corner Detection, K-Nearest Centroid Neighbor, Crowd Anomaly Detection, Real-Time Surveillance.Abstract
A problem still to solve with dense crowds was the detection of abnormal behavior, as the motion patterns are usually complex, there are often occlusions, and there was the requirement of real-time processes. A hybrid framework was suggested as a solution to these problems, which combines optical flow, motion heat maps, Harris corner detection, and a K-nearest centroid neighbor (K-NCN) classification procedure. Optical flow was used to record the information about continuous motion, and motion heat maps give a spatiotemporal interpretation of the activity level intensity and emphasize the suspicious areas more clearly. A more sophisticated feature extraction tool, Harris corner detection identifies clear motion key points used when there was a great cluttered scene in the picture; hence, it ensures that there was no overlap of movements. The extracted motion descriptors are subjected to a K-NCN classifier that effectively clusters the behaviors in the crowds and can be defined by the distance of the center point and calculates the anomaly score corresponding to the deviations to the learned behaviors. Such a combination does not only lead to increased detection accuracy but also retains fine computational efficiency, which was appropriate in real-time usage. The system also proves to be resilient amid varying forms of crowd situations because it was capable of differentiating between regular and abnormal collective motions through differentiating notional collective activities and those that have the possibility of indicating danger. The framework provides a desired practical solution that balances accuracy with speed to accomplish intelligent surveillance, safety monitoring of the population, and management of large events. Optical motion analysis, coupled with the centroid-based classification, mean the ability to respond to crowds of varying density and in varying settings, resulting in the proactive prevention of anomalies and situational awareness.
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