On the perceived location of global motion

We measured the effects of coherent motion of one set of dots on the perceived location of Gaussian envelopes formed by luminance modulation of a second set of dots. Perceived shifts in envelope location in the direction of coherent motion were obtained even when the dots forming the envelopes did not physically move in the direction of coherent motion. In such cases, perceived shifts coincided with stimulus configurations that permitted motion integration of the envelope dots with the coherently moving dots, for example, when envelope dots moved in random directions as opposed to being static. In subsequent experiments we explored the type of motion integration underlying the positional shifts obtained. We discounted the possibility that the visual system incorrectly attributes motion signals associated with coherently moving dots to envelope dots by demonstrating that positional shifts could be obtained even when the coherent dots were laterally displaced to either side of the envelope dots such that the regions occupied by the dots did not overlap. We also discounted spatio-temporal summation within the receptive fields of low-spatial-frequency motion-sensitive mechanisms by demonstrating that positional shifts persisted even when the dot displays were high-pass filtered. These results, coupled with the observation that the proportion of coherently moving dots required to produce positional shifts correlated well with global motion thresholds measured for the same dot configurations, suggests that visual processes which underlie motion-dependent positional shifts are based at least in part on cooperative interactions of the type implicated in global motion.

Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes

Global positioning system (GPS) technology has improved the speed, accuracy, and ease of time-motion analyses of field sport athletes. The large volume of numerical data generated by GPS technology is usually summarized by reporting the distance traveled and time spent in various locomotor categories (e.g., walking, jogging, and running). There are a variety of definitions used in the literature to represent these categories, which makes it nearly impossible to compare findings among studies.

The purpose of this work was to propose standard definitions (velocity ranges) that were determined by an objective analysis of time-motion data. In addition, we discuss the limitations of the existing definition of a sprint and present a new definition of sprinting for field sport athletes.

Twenty-five GPS data files collected from 5 different sports (men’s and women’s field hockey, men’s and women’s soccer, and Australian Rules Football) were analyzed to identify the average velocity distribution. A curve fitting process was then used to determine the optimal placement of 4 Gaussian curves representing the typical locomotor categories. 

Her Majesty moves: Sarah Bernhardt, Queen Elizabeth and the development of motion pictures

Sarah Bernhardt, the greatest theatrical star of the late nineteenth century, enabled and even promoted the association of early film with the British monarchy. She did this literally, by playing the role of Queen Elizabeth in Queen Elizabeth (Les Amours de la Reine Elisabeth, Henri Desfontaines and Louis Mercanton, 1912). Bernhardt also promoted the association of the cinema with monarchy symbolically, making the medium a new empathetic vehicle for the development of celebrity mystique and global power.

CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).