text: Action monitoring in a home environment provides important information for health monitoring and may serve as input into a smart home environment. Visual analysis using cameras can recognise actions in a complex scene, such as someones living room. However, although there the huge potential benefits and importance, specifically for health, cameras are not widely accepted because of privacy concerns. This paper recognises human activities using a sensor that retains privacy. The sensor is not only different by being thermal, but it is also of low resolution: 8x8 pixels. The combination of the thermal imaging, and the low spatial resolution ensures the privacy of individuals. We present an approach to recognise daily activities using this sensor based on a discrete cosine transform. We evaluate the proposed method on a state-of-the-art dataset and experimentally confirm that our approach outperforms the baseline method. We also introduce a new dataset, and evaluate the method on it. Here we show that the sensor is considered better at detecting the occurrence of falls and Activities of Daily Living. Our method achieves an overall accuracy of 87.50 across 7 activities with a fall detection sensitivity of 100 and specificity of 99.21 .
  @cite_15: Indoor tracking has all-pervasive applications beyond mere surveillance, for example in education, health monitoring, marketing, energy management and so on. Image and video based tracking systems are intrusive. Thermal array sensors on the other hand can provide coarse-grained tracking while preserving privacy of the subjects. The goal of the project is to facilitate motion detection and group proxemics modeling using an 8 x 8 infrared sensor array. Each of the 8 x 8 pixels is a temperature reading in Fahrenheit. We refer to each 8 x 8 matrix as a scene. We collected approximately 902 scenes with different configurations of human groups and different walking directions. We infer direction of motion of a subject across a set of scenes as left-to-right, right-to-left, up-to-down and down-to-up using cross-correlation analysis. We used features from connected component analysis of each background subtracted scene and performed Support Vector Machine classification to estimate number of instances of human subjects in the scene.
  @cite_10: We propose a human body tracking method using a far-infrared sensor array. A far-infrared sensor array captures the spatial distribution of temperature as a low-resolution image. Since it is difficult to identify a person from the low-resolution thermal image, we can avoid privacy issues. Therefore, it is expected to be applied for the analysis of human behaviors in various places. However, it is difficult to accurately track humans because of the lack of information sufficient to describe the feature of the target human body in the low-resolution thermal image. In order to solve this problem, we propose a thermo-spatial sensitive histogram suitable to represent the target in the low-resolution thermal image. Unlike the conventional histograms, in case of the thermo-spatial sensitive histogram, a voting value is weighted depending on the distance to the target’s position and the difference from the target’s temperature. This histogram allows the accurate tracking by representing the target with multiple histograms and reducing the influence of the background pixels. Based on this histogram, the proposed method tracks humans robustly to occlusions, pose variations, and background clutters. We demonstrate the effectiveness of the method through an experiment using various image sequences.
summary: Several features have been investigated specifically for low resolution infrared sensors, most notably @cite_15 . Here, connected component analysis was used to evaluate the number of individuals present in the scene, which subsequently led to motion tracking of the individuals; however this method was sensitive to background noise. A thermo-spatial sensitive histogram feature approach was able to reduce the noise from background pixels @cite_10 . Although counting and tracking of individuals is a non-trivial task, here we are concerned with the activity of each individual. Intuitively, this would appear to require finer detail, and this poses a difficult task given the low spatial resolution of the image.