The recent developments in video anomaly detection and privacy-preserving computer vision have shown a significant shift towards hybrid architectures and selective privacy techniques. Researchers are increasingly integrating spatial and temporal analyses within a single framework to enhance real-time anomaly detection capabilities. This integration leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process video sequences more effectively. Additionally, there is a growing emphasis on developing privacy-preserving methods that do not compromise the utility of the models. Techniques like masked differential privacy are being explored to selectively apply privacy measures, thereby achieving a better balance between privacy and model performance. Lightweight and adaptive anonymization methods are also emerging, offering real-time privacy protection without significantly impacting anomaly detection efficacy. These advancements collectively push the boundaries of what is possible in real-time video analysis while addressing critical privacy concerns.

Noteworthy papers include one that proposes a hybrid architecture combining VGG19 and YOLOv7 for spatial and temporal analysis, and another introducing masked differential privacy for selective data protection.