Anthony Whitehead

Exergame effectiveness: what the numbers can tell us

A sedentary lifestyle is linked to many diseases, including diabetes and heart disease, as well as ailments such as obesity, which is becoming the major root cause of early death in most industrialized countries. The advent of the television, computers and videogames has resulted in a more sedentary lifestyle, with more time spent in front of a screen more than ever before. Exergaming is a term used to describe video games that provide encouragement to exercise, particularly for an audience that may be reluctant to engage in the more traditional forms of exercise. Exergames are a commonly accepted method of encouraging more physical activity to promote better health for those with high levels of sedentary screen time. In this work, we survey a number of quantitative exergame studies to define a general set of elements that make exergames effective from a physical standpoint. We also examine the intended audience and the incentive elements necessary for an exergame to meet the needs of its audience. Finally, we examine our own exergame system and how well it performs against commercial systems.

Temporal Synchronization of Video Sequences in Theory and in Practice

In this work, we present a formalization of the video synchronization problem that exposes new variants of the problem that have been left unexplored to date. We also present a novel method to temporally synchronize multiple stationary video cameras with overlapping views that: 1) does not rely on certain scene properties, 2) suffices for all variants of the synchronization problem exposed by the theoretical disseration, and 3) does not rely on the trajectory correspondence problem to be solved apriori. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying inflection points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find moving features that best agree with the pre-computed camera geometries from stationary image features. By using the fundamental matrix and the trifocal tensor in the second refinement step we are able to improve the estimation of the first step and handle a broader range of input scenarios and camera conditions.

Feature Based Cut Detection with Automatic Threshold Selection

There has been much work concentrated on creating accurate shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. In this work we present a scheme based on stable feature tracking for inter frame differencing. Furthermore, we present a method to stabilize the differences and automatically detect a global threshold to achieve a high detection rate. We compare our scheme against other cut detection techniques on a variety of data sources that have been specifically selected because of the difficulties they present due to quick motion, highly edited sequences and computer-generated effects.

Persistent realtime building interior generation

A novel approach to generate virtual building interiors in real-time is presented. The interiors are generated in a top-down fashion using architectural guidelines. Although a building interior in its entirety may be quite large, only the portions that are needed immediately are generated. This lazy generation scheme allows the use of only a fraction of the memory that a model of the entire interior would otherwise require. Our method provides real-time frame rates, making it attractive for realtime interactive applications.Memory is controlled by deleting regions of the interior that are no longer needed. That said, any changes made in these regions will not be lost. We provide a simple and efficient method to allow changes made to the interior to persist past the life time of the regions that contain them. This allows a dynamic, consistent environment and increases control over the content by allowing developers to make changes.

Dance, Dance Evolution: Accelerometer Sensor Networks as Input to Video Games

We have created and tested a wearable sensor network that detects a users body position as input for video game applications. It is envisioned to take video game experiences such as Dance Dance Revolution to a whole new level, replacing the binary foot-pad with a more immersive, full-body input system. We describe the design and functionality of the sensor network and experiment with Mahalanobis distance as a nearest-neighbour means of classification. Results from our experiments with distance threshold levels, combined data sets and the effects of practice on user success rates are discussed.

Sensor networks as video game input devices

In this work we are motivated by creating a network of sensors that can be used as input devices for video games. Our goal is to create an inexpensive network of off-the-shelf sensors that are used to force proper movement and engagement of the player. Our experience shows that a distributed set of sensors around the body prevents the player from cheating the system by using motion of the device alone to trick the system. In this work we show that a relatively simple sensor network configuration can enforce proper form and ensure that the player is actively participating in the game context.

Robust Horizon Detection Using Segmentation for UAV Applications

A critical step in navigation of unmanned aerial vehicles is the detection of the horizon line. This information can be used for adjusting flight parameters as well as obstacle avoidance. In this paper, a fast and robust technique for precise detection of the horizon path is proposed. The method is based on existence of a unique light field that occurs in imagery where the horizon is viewed. This light field exists in different scenes including sea-sky, soil-sky, and forest-sky horizon lines. Our proposed approach employs segmentation of the scene and subsequent analysis of the image segments for extraction of the mentioned field and thus the horizon path. Through various experiments carried out on our own dataset and that of another previously published paper, we illustrate the significance and accuracy of this technique for various types of terrains from water to ground, and even snow-covered ground. Finally, it is shown that robust performance and accuracy, speed, and extraction of the path as curves (as opposed to a straight line which is resulted from many other approaches) are the benefits of our method.

Estimating Intrinsic Camera Parameters from the Fundamental Matrix Using an Evolutionary Approach

Calibration is the process of computing the intrinsic (internal) camera parameters from a series of images. Normally calibration is done by placing predefined targets in the scene or by having special camera motions, such as rotations. If these two restrictions do not hold, then this calibration process is called autocalibration because it is done automatically, without user intervention. Using autocalibration, it is possible to create 3D reconstructions from a sequence of uncalibrated images without having to rely on a formal camera calibration process. The fundamental matrix describes the epipolar geometry between a pair of images, and it can be calculated directly from 2D image correspondences. We show that autocalibration from a set of fundamental matrices can simply be transformed into a global minimization problem utilizing a cost function. We use a stochastic optimization approach taken from the field of evolutionary computing to solve this problem. A number of experiments are performed on published and standardized data sets that show the effectiveness of the approach. The basic assumption of this method is that the internal (intrinsic) camera parameters remain constant throughout the image sequence, that is, the images are taken from the same camera without varying such quantities as the focal length. We show that for the autocalibration of the focal length and aspect ratio, the evolutionary method achieves results comparable to published methods but is simpler to implement and is efficient enough to handle larger image sequences.

Image Registration Under Illumination Variations Using Region-Based Confidence Weighted

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M-estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.

M

This paper presents a new method for obstacle detection using optical flow. The method employs a highly efficient and accurate adaptive motion detection algorithm for determining the regions in the image which are more likely to contain obstacles. These regions then have optical flow performed on them. We call this method targeted optical flow. Targeted optical flow performs significantly faster compared to regular optical flow. We employ two types of optical flow to demonstrate the performance and speed increase of the proposed system. Finally, k-means clustering is employed for obstacle reconstruction. The system is designed for color videos for better performance. Several benchmark and recorded sequences have been used for testing the system.