A survey on Abnormal Detection in the Crowd

Abstract

This article discusses employing advanced technologies to track the actions of unusual people in busy places, e.g. weird hand and body motions, and various facial expressions, all of which represent a potential security threat spot or some danger coming ahead. The main goal of the study is to integrate smartest precautions in the areas like public places, healthcare centers, and the traffic infrastructure via the use of improved algorithms, machine learning systems, deep neural networks and computer vision. In this case, the study relies on the use of models running convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which can analyze the video data and detect the anomalies in real-time, leading to PTZ, mobilization to monitor detected abnormal activities.

The article shows that the current techniques have been explored, discussing spatial feature extraction better in CNNs and LSTM networks better in temporal sequence analysis. It also, suggests the interaction of Generative Adversarial Networks (GANs) with the optical flow technique for more accurate anomaly detection in busy places with special stress on big population areas such as the Hajj pilgrimage.

Besides, the research investigates the option of Unmanned Aerial Vehicles (UAVs) for monitoring the crowd, laying emphasis on the necessity of policy as well as ethical considerations in drone surveillance. One of the key points raised in the study is the support to the real-time anomaly detection systems by hybrid models that combine several deep learning technologies to improve the accuracy and efficiency of such systems. The evidence provided supports the argument that the improvement of these technologies is going to bring about a noticeable change in the area of public safety and security through offering flexible and concrete monitoring solutions.