DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter.

Unlike deep learning which requires large training datasets, correlation filter-based trackers like Kernelized Correlation Filter (KCF) use implicit properties of tracked images (circulant structure) for training in real time. Despite their popularity in tracking applications, there exists significant drawbacks of the tracker in cases like occlusions and out-of-view scenarios. This paper attempts to address some of these drawbacks with a novel RGB-D Kernel Correlation tracker in target re-detection.

Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction.

Tracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped and visualized using a virtual avatar model of the tracked person.

Passive Observer of Activities for Aging in Place Using a Network of RGB-D Sensors.

Aging in place is a notion which supports the independent living of older adults at their own place of residence for as long as possible. To support this alternative living which can be in contrast to various other types of assisted living options, modes of monitoring technology need to be explored and studied in order to determine a balance between the preservation of privacy and adequacy of sensed information for better estimation and visualization of movements and activities. In this paper, we explore such monitoring paradigm on how a network of RGB-D sensors can be utilized for this purpose.

Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults.

It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy.

Deep Attention Models for Human Tracking Using RGBD.

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects.

Effects of assembling virtual fixtures on learning a navigation task.

An approach to enhance navigation task performance is to integrate sensory guidance (virtual fixtures) into a virtual training system. To evaluate the effects of adding virtual fixtures to skill acquisition, 32 subjects were required to use a PHANToM input device, to transport a virtual object through a computer generated 3-D graphic maze. Subjects practiced navigation under 4 conditions: the maze was augmented with either a graphic fixture (G), attractive force field (F), both graphic and force field (GF), or no (N) virtual fixture.

Fuzzy classification: towards evaluating performance on a surgical simulator.

Computer-based surgical simulators such as the MIST-VR are able to provide scoring metrics such as time taken to complete a task, number of errors made, and economy of movement. Using MIST-VR's basic metrics, we explored the possibility of classifying skill levels using fuzzy logic. Our objective was to create a fuzzy classifier capable of classifying the performance of a subject training on a surgical simulator into 1 of 3 categories: Novice, Intermediate, and Expert.

Acquiring laparoscopic manipulative skills: a virtual tissue dissection training module.

Virtual environments for training manipulative skills in laparoscopic surgery are now well established as research areas. Tissue dissection however has not yet been adequately addressed. We have developed a virtual training module in which the task is to dissect a 3D tissue model using a simulated L-hook.

On defining metrics for assessing laparoscopic surgical skills in a virtual training environment.

One of the key components of any training environment for surgical education is a method that can be used for assessing surgical skills. Traditionally, defining such a method has been difficult and based mainly on observations. However, through advances in modeling techniques and computer hardware and software, such methods can now be developed using combined visual and haptic rendering of a training scene.