K-fold matching model for crowd behavioral anomaly detection from discontinuous inputs

Abstract

Image and associated features detect behavioural anomalies in crowd scenes through matching. Existingmethods use continuous data inputs to track crowd behaviour. As a result, the systemmistakenly assumes normal behaviour during data gaps, leading to false positives or missing detections of small abnormalities. The design of precision-focused matching requires simplified yet robust validation for any number of crowd elements. This article presents a K-fold Matching Model (KFMM) with edge-to-edge correlation. This proposed model identifies the region edges of an input crowd image sequentially. The difference in edges due to crowd misbehaviour activities is validated using random and continuous image features in K number of steps. Considering the complexity of multiple K-steps, the confinement is performed using a neural sigmoid function where the K × K edge matching is reduced. This reduction is based on activity classification i.e. minimum and maximum between two consecutive input sequences. Therefore, as the classification increases, the K is halted with the maximum correlation; contrarily for a low number of classifications, the manifold matching is pursued until the maximum is reached. This correlation count is defined using the neural sigmoid with a linear function for the edges. For the maximum features, this proposed model achieves an 11.21 % high matching ratio, 8.15 % high classifications, 9.71 % high precision, 9.61 % less complexity, and 8.9 % less linear error.