Categorizing Crowd Emotions based on Cross Division Expressions and Anomalies

The crowd emotion sensing is a critical element in surveillance and management of the crowd in different environments. With exploding populations, and developing nations, the crowd in urban cities mandate state of art surveillance methodologies involving continuous monitoring and reporting of criminal activities. The research article presents a novel technique to compute the spatial and temporal features obtained from the crowd environments and combine the novelty of neural networks for detecting the emotions of crowds with better accuracy and swiftness. The features are obtained from the continuous feed of surveillance videos typically categorized into the common features of human beings namely anger, sadness, disgust, surprise, fear, happiness and obviously neutrality. Such features are extracted after careful background separation which are typically difficult in crowded environments, using techniques namely SIFT, and FAST termed to be the visual descriptors. Once the features are extracted, spatial and temporal features are classified into individual and combined features as defined in the cross-division environment in order to portray the crowd dynamics and characteristics. Cross division environment computes the necessary features for identifying the anomalies in the crowded situations in a neural network, after a series of operations such as dimensionality reduction, and principal component analysis. From the semantic information, crowd behaviours are detected based on interactive features in a dynamic environment and the proposed technique has demonstrated effective results in terms of 98.9% accuracy in detecting especially violence in crowd datasets collected from UMN.