Video Anomaly Detection Utilizing Efficient Spatiotemporal Feature Fusion with 3D Convolutions and Long Short‐Term Memory Modules

Surveillance cameras produce vast amounts of video data, posing a challenge for analysts due to the infrequent occurrence of unusual events. To address this, intelligent surveillance systems leverage AI and computer vision to automatically detect anomalies. This study proposes an innovative method combining 3D convolutions and long short‐term memory (LSTM) modules to capture spatiotemporal features in video data. Notably, a structured coarse‐level feature fusion mechanism enhances generalization and mitigates the issue of vanishing gradients. Unlike traditional convolutional neural networks, the approach employs depth‐wise feature stacking, reducing computational complexity and enhancing the architecture.…

• MO
  Ministry of Trade, Industry and Energy
• MO
  Ministry of Culture, Sports and Tourism
• KI
  Korea Institute for Advancement of Technology

  Award: P0020632
• KC
  Korea Creative Content Agency