Image Transformation Approaches for Occupancy Detection: A Comprehensive Analysis

Using occupancy information in building management can help save energy and maintain user comfort, which is particularly important as energy becomes scarce and people rely more on appliances. While camera-based occupancy detection is widely adopted due to its efficacy, it also brings to the forefront a range of privacy-related issues that merit consideration. Therefore, this study proposes a method that uses environmental data to identify occupancy patterns. The technique converts time-series data into images to improve feature extraction and enhance the accuracy of occupancy detection. Three image transformation techniques are compared in the study, and the grayscale approach achieved the highest accuracy of 98.09%. In contrast, the Gramian Angular Summation Fields (GASF) along with the Gramian Angular Difference Fields (GADF) approaches had lower but still reasonable accuracy levels of 97.38% and 97.64%, respectively. The required training time for all three techniques was similar. These results suggest that the proposed grayscale approach is a suitable and efficient method for transforming images and detecting binary occupancy data.