Intelligent Detection of Suspicious Human Activities through CNN Integration

Abstract—With the unprecedented development of technolo- gies, there has been the trend of rapid deployment of surveillance systems in multiple domains, such as security and healthcare, and public safety. In this paper, a new method of suspicious human activity detection based on Convolutional Neural Net- works (CNNs) combined with Long Short-Term Memory (LSTM) networks is proposed. The presented system seeks to improve the robustness and speed of anomaly detection across real-time video streams. Leveraging CNNs for spatial feature extraction and LSTMs for temporal sequence modeling, the proposed system efficiently detects suspicious activities in real time. Index Terms—Suspicious Activity Detection, Human Activity Recognition, Convolutional Neural Networks, Deep Learning, Video Surveillance