SqueezeNet-ImpLinknet Architecture for Crowd Anomaly Detection With Improved R-CNN-Based Segmentation

Abstract

Crowd anomaly detection is a critical aspect of ensuring public safety in various domains such as surveillance and security. Ensuring public safety in crowded environments requires accurate and efficient crowd anomaly detection. This research proposes an innovative approach to crowd anomaly detection using the SqueezeNet-ImpLinknet architecture. The input images are first preprocessed using a median filtering technique. Then, object segmentation takes place using an Improved Mask Region-based CNN. It incorporates batch normalization, ReLU activation, and an advanced Scale Dot Product attention mechanism to improve segmentation accuracy and computational efficiency. Subsequently, features such as the Improved SLBT feature, capturing shape and texture information, color features, and LGTrP features are extracted. Then, anomaly detection is performed using a hybrid model that integrates SqueezeNet and Improved Linknet models. The Improved LinkNet model enhances feature representation by integrating an attention mechanism in the encoder and a novel ReLUSignmax activation function in the decoder, overcoming limitations of conventional architectures. The approach is evaluated on the widely used UCSD Anomaly Detection Dataset, achieving superior performance with accuracy ranging from 0.939 to 0.975 and a specificity of 0.987 at 90% training data. The proposed approach offers a robust solution for intelligent surveillance in crowded environments.