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Automatic Anomaly detection in a crowd scene is very significant because of more apprehension with people's safety in a public place. Because of usefulness and complexity, currently, it is an open research area. In this work, a new Convolutional Neural Network (CNN) model is proposed to detect crowd anomaly. Experiments are carried out on two publicly available datasets. The performance is measured by Accuracy and Area Under the ROC Curve (AUC). The experimental results determine the efficacy of the proposed model.