This paper addresses the problem of on-line learning for object tracking. Although a variety of techniques have been proposed in literature, a recent benchmark reveals that none of them can work well in all scenarios due to numerous practical challenges, such as illumination variations, motion blur, etc. These challenges occur at different time frames making it hard to design a tracker. In this paper, a machine learning framework for object tracking is investigated, which can integrate with a variety of feature and kernel engineering techniques in dealing with many challenges in different scenarios. By following the successful tracking-by-detection methodology, this paper proposes OMKT - On-line Multiple Kernel Tracking scheme, which attempts to tackle the object tracking task by exploring recent advances of on-line multiple kernel learning techniques in machine learning. In particular, OMKT sequentially learns the best tracker for each individual kernel via on-line Projectron++ learning, and at the same time attempts to identify the optimal combination of multiple kernel trackers using the Hedge algorithm. In contrast to many existing schemes in literature, OMKT learns both the kernel classifiers and their combination on-line, hence it can adapt to tracking changes faster. Furthermore, the projection strategy in Projectron++ alleviates the difficulty of pre-specifying a budget size for support-set. Promising experimental results on a recent benchmark reveal usefulness of OMKT.

This paper proposes a new algorithm for the multiple instance learning problem (MIL) and investigates its application for detecting Attention Deficit Hyperactive Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging data. The core component of many kernel-based MIL algorithms is usually an SVM-like batch optimization framework, hence scaling to large datasets like fMRI is often difficult. On the other hand, a family of on-line kernel classification algorithms widely known as “perceptron-like” kernel classifiers demonstrate efficient and accurate solutions. This paper presents MiPAL - Multiple-instance Passive Aggressive Learning algorithm, based on such a perceptron-like kernel classifier. First, MiPAL builds a labeller by inputting negative bags into PA algorithm. Second, this labeller helps to train a separate PA classifier to predict binary class labels such that least-negative instances are regarded as positive. Due to the on-line PA algorithm's fast adaptation, the impact of invalid positive support-vectors could be attenuated by the new, accurate support-set over time. Our experimental results reveal performance gains in several MIL datasets including state-of-the-art performance in Muskl, Fox, and comparable accuracy in the preprocessed ADHD-200 dataset.

Radio Tomographic Imaging (RTI) enables device free localization of physical objects by using signal attenuation in wireless networks. In this paper, we explore how existing RTI methods can be used in WiFi networks to do tomographic imaging. Moreover we analyze and evaluate the properties that affect the accuracy of WiFi tomographic imaging process.

We describe a real time authoring tool that is useful to supervise group of users in an augmented reality environment. The overall system is composed of a server; the Command Center (CC) and several clients; Wearable Systems (WS). The server is used to visualize the virtual representation of the real environment, 3d features map where augmentation are to occur, augmented content that will be rendered and users avatar that evolves on the field. The user's WS is equipped of a Head Mounted Display (HMD) along with sensors (GPS/DRM/Compass) to provide constant pose estimation (position and orientation) to the CC and a vision based tracking that uses natural features and pose estimation allgorithms to render virtual content accordingly. Alternatively a sensor based renderer is used to display AR guidance on the HMD based on the path defined and sent from the CC. The natural features system handles a large number of small 3D features maps. The overall system offers a complete system to train and supervise remote users through an AR environment.