Human activity recognition using limb component extraction

in the field of human activity recognition has existed for quite sometime, but has gained popularity in recent years for use in many areas of application. In the security industry, suspicious activities could be detected in high-profile areas. In the medical industry, systems could be trained to detect patterns of motion indicating distress or to detect a lack of motion if a person had fallen and was unable to move. However, algorithms with reliable accuracy are difficult to implement in a real-time environment due to computational complexity. This thesis developed a new way of extracting and using data from a human figure in a video frame to determine what type of activity the subject is performing. Following background subtraction, a thinning algorithm operating on the silhouette offered a more robust limb extraction method, while a six-segment representation of the human figure offered more accuracy in deriving limb parameters, or components, such as distance from torso, and angle of displacement from the vertical axis. Neural networks or nearest neighbor classifiers used the limb components to identify a number of activities, such as walking, running, waving and jumping. This entire human activity recognition system was tested with both a MATLAB implementation (non real-time) and a C++ implementation in OpenCV (real-time). The algorithm achieved 96% classification accuracy in video feeds, which is only slightly lower than that of intensive, non real-time systems

Rastreo automático del movimiento de extremidades orientado al análisis funcional de las articulaciones en pacientes con secuelas de quemaduras.

Este trabajo muestra el desarrollo de un sistema para la medición automática en 2D y 3D, de la amplitud de la movilidad articular orientado al análisis de su funcionalidad. El sistema también está en capacidad de reconstruir automáticamente la trayectoria en 3D (a partir de múltiples vistas) de las extremidades comprometidas en el proceso de evaluación. Adicionalmente, se realizaron pruebas de reconstrucción orientadas al análisis de la postura. La automatización se logró gracias a la incorporación de técnicas de procesamiento digital de imágenes, dentro de las cuales se pueden resaltar el análisis de histogramas para el realce del contraste, la segmentación por medio de análisis de conectividad, umbralización dinámica, entre otras. Las imágenes procesadas corresponden a fotografías de alta resolución, y a imágenes extraídas de secuencias de video filmadas con cámaras: de videos convencionales y Web. La herramienta desarrollada requiere de la colocación de un marcaje en las extremidades a ser evaluadas, con lo cual se maximiza la probabilidad de que la medición y la reconstrucción se realicen correctamente. Los resultados muestran que el sistema posee una precisión adecuada para este tipo de aplicaciones / Abstract: This work shows the development of a system for the automatic measurement in 2D and 3D, of the joint mobility amplitude oriented to the analysis of its functionality. The system also is in capacity of reconstruct the trajectory in 3D (from multiple view points) of the limbs related in the evaluation process. Additionally, test of reconstruction oriented to the posture analysis, were made. The automatization of the measure process was obtained by mean of the techniques of digital image processing, within which the analysis of histograms for the enhancement of the contrast, segmentation by means of connectivity analysis, dynamic thresholding, among others. The processed images correspond to digital photographies in hi-res, and extracted images of filmed sequences of video with two kind of cameras: digital video and Web. The developed tool requires of the positioning of a body markers in the limbs to be evaluated, with which the probability that correctly: measurement and reconstruction, is maximized. The results show that the system has a precision accepted for this kind of applications.Maestrí