A Hybrid Framework for Temporal Object Behavior Analysis Using LSTM and Real-Time Detection

Fall detection in surveillance videos is a critical task with widespread applications in healthcare and public safety. In this paper, we propose a hybrid framework that combines YOLOv8 for real-time object detection with an LSTM based temporal reasoning module to classify human activities across video sequences. Our method captures both spatial appearance and temporal motion patterns, allowing it to distinguish subtle differences between activities such as standing, walking, lying, and falling. We evaluate our model on the UR Fall Detection (URFD) dataset and achieve a validation accuracy of 92.0% and a mean Average Precision (mAP@0.5) of 91.2%. Qualitative results further demonstrate robust predictions even in challenging scenarios with occlusions and lighting variations. An ablation study confirms that integrating temporal reasoning significantly boosts performance over frame-based detection alone. The proposed approach offers a promising solution for real-time fall detection in intelligent surveillance and assistive monitoring systems.