Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Experimental and Numerical Investigation of Post-Flutter Limit Cycle Oscillations on a Cantilevered Flat Plate

Futuristic aircraft designs and novel aircraft such as High Altitude Long Endurance (HALE) involve a higher level of structural flexibility than in conventional aircraft. Even at present, the trends in the aviation industry are to increase wing length (to reduce induced drag) and maxi- mize use of composites, which lead to increased structural flexibility. This necessitates a rethink of conventional (linear) aeroelastic analysis, since the increased flexibility results in coupling between the flight dynamic and aeroelastic dynamics, and consequently, limit-cycle oscillations of the structure. In this paper, a new three-dimensional low-order model for unsteady aerody- namics that accounts for large oscillation amplitudes and nonplanar wakes is developed. An experiment with a cantilevered flat plate at low Reynolds number is set up and used to validate the low-order model, as well as to study post-flutter limit-cycle oscillations. Results from the low-order model are promising, but show that aerodynamic nonlinearities such as flow sepa- ration and leading-edge vortex shedding must also be modeled in order to predict all possible limit-cycle oscillations of the aeroelastic system

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project