This study investigates the integration of audio features with CO₂ sensor data to enhance occupancy detection accuracy in naturally ventilated office environments. Accurate occupancy detection is pivotal for smart building energy management, yet CO₂-based methods cannot provide fast enough response times and are sensitive to air circulation changes due to internal convection. In this article we propose a combination of CO₂ sensors and audio features from MEMS microphones to improve the occupancy detection accuracy and improve the response times. We use a Random Forest classifier and evaluate the results across two scenarios: CO₂-only and CO₂ combined with audio features. Results show that incorporating the audio features into the occupancy detection algorithms yields a significant increase in detection accuracy and speed, especially when the environment is subject to frequent air circulation changes due to internal convection, like the opening and closing of windows and doors. Combining the CO₂ and audio sensing offers a promising, cost-effective approach to occupancy detection in smart buildings, yet more research on advanced audio processing and feature selection is necessary.

Vision-based traffic monitoring recovers metric quantities such as inter-vehicle distances by mapping the image to the ground plane through a planar homography. In deployed systems, this homography is often fitted from ground control points whose world coordinates are obtained from satellite orthoimagery, where each coordinate is uncertain by about one image pixel. The impact of this control-point coordinate error on the measured distance has not been quantified for a single fixed camera. This paper evaluates a 180-degree panoramic camera (AXIS P3807-PVE) on two adjacent ground-plane regions, each referenced to RTK-surveyed control points and mapped by a separate homography. Control-point coordinates are perturbed with isotropic Gaussian noise, the homography is refitted, and the resulting change in inter-point distance between held-out evaluation points is measured against the unperturbed fit. The distance error is dominated by random scatter rather than systematic bias, and the scatter grows approximately linearly with the coordinate noise, at about 0.7 to 0.8 times the per-coordinate standard deviation within the tested range. The results indicate that distance error can be predicted from control-point coordinate accuracy, and that relative precision of those coordinates matters more than absolute georeferencing.