A comparison of head and manual control for a position-control pursuit tracking task

Head control was compared with manual control in a pursuit tracking task involving proportional controlled-element dynamics. An integrated control/display system was used to explore tracking effectiveness in horizontal and vertical axes tracked singly and concurrently. Compared with manual tracking, head tracking resulted in a 50 percent greater rms error score, lower pilot gain, greater high-frequency phase lag and greater low-frequency remnant. These differences were statistically significant, but differences between horizontal- and vertical-axis tracking and between 1- and 2-axis tracking were generally small and not highly significant. Manual tracking results were matched with the optimal control model using pilot-related parameters typical of those found in previous manual control studies. Head tracking performance was predicted with good accuracy using the manual tracking model plus a model for head/neck response dynamics obtained from the literature

Contour tracking of contaminant clouds with sequential Monte Carlo methods

Contour tracking for a single source emission is addressed in this paper. This problem is solved by estimating the contour boundary positions using a set of particle filters. The use of Sequential Monte Carlo techniques enables the tracking to performed when the measurements are noisy and the tracking results also includes the estimation uncertainly. The proposed technique is illustrated for a SCIPUFF generated single emission scenario and simulation experiments showed the successful tracking throughout the tracking period

Distributed tracking with sequential Monte Carlo methods for manoeuvrable sensors

Nonlinear distributed tracking for a single target is addressed in this paper. This problem consists of tracking a target of interest while moving the sensors to `best' positions according to an critera appropriate for the problem. Both target tracking and manoeuvring of sensors are carried out jointly using a novel Sequential Monte Carlo technique. The proposed technique is illustrated using a bearing-only problem and simulations are used to compare the performance of the proposed technique with distributed tracking using fixed sensors.Nonlinear distributed tracking for a single target is addressed in this paper. This problem consists of tracking a target of interest while moving the sensors to `best' positions according to an critera appropriate for the problem. Both target tracking and manoeuvring of sensors are carried out jointly using a novel Sequential Monte Carlo technique. The proposed technique is illustrated using a bearing-only problem and simulations are used to compare the performance of the proposed technique with distributed tracking using fixed sensors

Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.National Science Foundation (IIS-0329009, IIS-0093367, IIS-9912573, EIA-0202067, EIA-9809340

Multiview System for Tracking a Fast Moving Object Against Complex Backgrounds

Tracking the real world coordinate of a fast moving object against a complex background is very challenging. When designing a multi-view system for this purpose, one key consideration is the arrangement of the cameras such that the object can be constantly and accurately tracked. This paper discusses a novel cameras arrangement, which can provide redundancy for fault tolerance, yet do not require installing more cameras nor relying aerial views of the scene. Using a table tennis match as example, experiment results show that the multi-view system with this cameras arrangement has a promising potential for tracking a table tennis ball in a real match scene