We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for exhaustive on-line search, this work aims to select models that are suitable for a particular tracking task at hand. During an off-line training stage observation models from various off-the-shelf trackers are evaluated. From this data different methods of fusing the observers on-line are investigated, including parallel and cascaded evaluation. Experiments on test sequences show that this evaluation is usefulfor automatically designing and assessing algorithms for a particular tracking task. Results are shown for face tracking with a handheld camera and hand tracking for gesture interaction. We show that for these cases combining a small number of observers in a sequential cascade results in efficient algorithms that are both robust and precise.

Abstract

This paper proposes an algorithm for online feature selection which improves robustness to occlusions by referring to a localized generative appearance model. Discriminative classifiers based on feature extraction have classically either prepared a fixed prior model by training offline, or continually adapted their classification parameters to any apparent appearance changes. By combining the attractive qualities of each approach, our framework can cope with appearance changes of a target object and will maintain proximity to a static appearance model. Our main contribution is the use of a generative model to guide the online feature selection to regions of an image which maintain a valid appearance. The generative model exhibits the properties of non-negativity, localization and orthogonality. We demonstrate the system in a tracking framework to show improved tracking performance through occlusions.

This paper presents a vision-based system for interaction with a display via hand pointing. An attention mechanism based on face and hand detection allows users in the camera's field of view to take control of the interface. Face recognition is used for identification and customisation. The system allows the user to control the screen pointer by tracking their fist. On-screen items can be selected using one of four activation mechanisms. Current sample applications include browsing image and video collections as well as viewing a gallery of 3D objects. In experiments we demonstrate the performance of the vision components in challenging conditions and compare it to that of other systems.

This chapter presents a vision-based system for touch-free interaction with a display at a distance. A single camera is fixed on top of the screen and is pointing towards the user. An attention mechanism allows the user to start the interaction and control a screen pointer by moving their hand in a fist pose directed at the camera. On-screen items can be chosen by a selection mechanism. Current sample applications include browsing video collections as well as viewing a gallery of 3D objects, which the user can rotate with their hand motion. We have included an up-to-date review of hand tracking methods, and comment on the merits and shortcomings of previous approaches. The proposed tracker uses multiple cues, appearance, color, and motion, for robustness. As the space of possible observation models is generally too large for exhaustive online search, we select models that are suitable for the particular tracking task at hand. During a training stage, various off-the-shelf trackers are evaluated. From this data different methods of fusing them online are investigated, including parallel and cascaded tracker evaluation. For the case of fist tracking, combining a small number of observers in a cascade results in an efficient algorithm that is used in our gesture interface. The system has been on public display at conferences where over a hundred users have engaged with it.