How Low Can You Go? Performance Trade-offs in Low-Resolution Thermal Sensors for Occupancy Detection: A Systematic Evaluation

We contribute by systematically analysing the performance trade-offs, costs (privacy loss and deployment cost) and limits of low-resolution thermal array sensors for occupancy detection. First, to assess performance limits, we manipulate the frame rate and resolution of images to establish the lowest possible values where reliable occupancy information can be captured. We also assess the effect of different viewing angles on the performance. We analyse performance using two datasets, an open-source dataset of thermal array sensor measurements (TIDOS) and a proprietary dataset that is used to validate the generality of the findings and to study the effect of different viewing angles. Our results show that even cameras with a 4x2 resolution - significantly lower than what has been used in previous research - can support reliable detection, as long as the frame rate is at least 4 frames per second. The lowest tested resolution, 2x2, can also offer reliable detection rates but requires higher frame rates (at least 16 frames per second) and careful adjustment of the camera viewing angle. We also show that the performance is sensitive to the viewing angle of the sensor, suggesting that the camera's field-of-view needs to be carefully adjusted to maximize the performance of low-resolution cameras. Second, in terms of costs, using a camera with only 4x2 resolution reveals very few insights about the occupants' identity or  behaviour, and thus helps to preserve their privacy. Besides privacy, lowering the resolution and frame rate decreases manufacturing and operating costs and helps to make the solution easier to adopt. Based on our results, we derive guidelines on how to choose sensor resolution in real-world deployments by carrying out a small-scale trade-off analysis that considers two representative buildings as potential deployment areas and compares the cost, privacy and accuracy trade-offs of different resolutions.Peer reviewe

Intelligently controlling HVAC with IoT technology

Heating, ventilation, and air conditioning (HVAC) systems consume massive amounts of energy. Fortunately, by carefully controlling these systems, a significant amount of energy savings can be achieved. This requires detecting a presence or amount of people inside the building. Countless different sensors can be used for this purpose, most common being air quality sensors, passive infrared sensors, wireless devices, and cameras. A comprehensive review and comparison are done for these sensors in this thesis. Low-resolution infrared cameras in counting people are further researched in this thesis. The research is about how different infrared camera features influence counting accuracy. These features are resolution, frame rate and viewing angle. Two systems were designed: a versatile counting algorithm, and a testing system which modifies these camera features and tests the performance of the counting algorithm. The results prove that infrared cameras with resolution as low as 4x2 are as accurate as higher resolution cameras, and that frame rate above 5 frames per second does not bring any significant advantages in accuracy. Resolution of 2x2 is also sufficient in counting but requires higher frame rates. Viewing angles need to be carefully adjusted for best accuracy. In conclusion, this study proves that even the most primitive infrared cameras can be used for accurate counting. This puts infrared cameras in a new light since primitive cameras can be cheaper to manufacture. Therefore, infrared cameras used in occupancy counting become significantly more feasible and have potential for widespread adoption