Motion Detection in Low Resolution Video Surveillance Data to Provide Personal Privacy

Video surveillance systems play a crucial role in ensuring security; however, they also raise concerns about personal privacy. This study focuses on motion detection in low-resolution video surveillance data while preserving privacy. The proposed approach involves converting a video clip into high-resolution frames, followed by downscaling them into low-resolution frames to obscure identifiable details. Motion detection is achieved through the frame subtraction method, where the difference between consecutive frames is computed to identify moving objects. The resulting motion is represented as a binary (black-and-white) frame, highlighting only dynamic regions while minimizing exposure of personal features. This method effectively balances security and privacy by enabling motion tracking without revealing sensitive details. The use of low-resolution frames ensures that surveillance footage does not compromise individual identity while maintaining the effectiveness of motion detection. The proposed technique can be beneficial in public surveillance scenarios where privacy concerns are paramount, such as smart cities and workplaces. Additionally, the simplicity of the frame subtraction method allows for real-time implementation with minimal computational requirements, making it suitable for resource-constrained environments. Future work will focus on enhancing detection accuracy and integrating advanced privacy-preserving techniques. 

Keywords: Dataset, Image Processing Techniques.