During the last two months the work has been focused on the scientific and technical aspects regarding person detection and tracking, and gesture spotting.

For the person detection, the work is further divided into face detection and tracking and body features detection and tracking.

In face detection, the work has been focused on achieving an automatic initialization for an Active Appearance Model based facial feature tracking approach, which is a practical problem of this approach. A novel solution has been proposed using LBP and Haar-features to detect the face and eye regions. And once detected, the rest of the facial points corresponding to the eyebrows, nose and the mouth are extracted. This approach improves the accuracy of facial point detection and performance since there is no need to generate and fit a model (AAM) with many parameters.

In body detection, the focus has been on removing the body detection and tracking limitations of OpenNI/NITE based body tracking implementation under difficult scenarios, such as people far away or too close to a wall. This improvement uses HOG descriptors and a pre-trained person detector, which provides a sparse detection (i.e. bounding box) which can be refined later on. And to improve the tracking procedure, a Rao-Blackwellized Data Association Particle Filter (RBDAPF) has been implemented, which can couple with sparse detections.

The body and face detection provides a constant flow of features set representing the pose and facial expressions. These information needs to be processed in order to recognize the body expression. Gesture spotting refers to the procedure of dividing a time series of features containing a set of a priori unknown set of gestures into non-overlapping pieces containing only one gesture each. The output pieces of this procedure are then classified into gestures. Two different algorithms for gesture spotting are being implemented which improve over the state of the art: Aligned Cluster Analysis (ACA) and its extension Hierarchical Aligned Cluster Analysis (HACA). Additionally, Y-Means is being implemented as a possible improvement over HACA/ACA algorithms or as alternative to them.