Calibration-Free Estimation of User-Specific Bending of a Head-Mountable Device

Disclosed is an approach for determining camera rotation relative to an individual’s eye, which could then be used for applications such as calibration-free estimation of user-specific bending of a device (e.g., a head-mountable device (HMD)), among others. Due to biological conditions, an average gaze vector of an eye most often corresponds to a straight-forward gaze by an individual. Therefore, the average gaze vector is often known with respect to a coordinate system of an individual’s eye. Additionally, when the HMD is worn by a user, the HMD uses an eye-facing camera to determine the average gaze vector of the user’s eye with respect to the eye-facing camera’s coordinate system. Given information about the average gaze vector with respect to multiple coordinate systems, the HMD uses this information as basis for determining an orientation of the eye-facing camera when the HMD is worn by the user. By then comparing the determined orientation to a known orientation of the eye-facing camera when the HMD is unworn or otherwise not bent, the HMD determines rotation of the eye-facing camera in three-dimensional space, which corresponds to an extent that the HMD (e.g., the HMD’s frame) has bent from an unworn position to a worn position

Modeling User Transportation Patterns Using Mobile Devices

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data. This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance. Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality

Evaluating Trust-Based Fusion Models For Participatory Sensing Applications

Participatory sensing is a specialized form of crowdsourcing for mobile devices in which the users act as sensors to report on local environmental conditions, such as traffic, pollution, and wireless signal strength. This computing framework has great promise as a tool for urban planners, but deploying new applications is a challenge since the overall performance can be sensitive to the specific user population. This paper describes the process of prototyping a mobile phone crowdsourcing app for monitoring parking availability on a large university campus

Online Learning of User-Specific Destination Prediction Models

Abstract—In this paper, we introduce and evaluate two different mechanisms for efficient online updating of user-specific destination prediction models. Although users can experience long periods of regular behavior during which it is possible to leverage the visitation time to learn a static user-specific model of transportation patterns, many users exhibit a substantial amount of variability in their travel patterns, either because their habits slowly change over time or they oscillate between several different routines. Our methods combat this problem by doing an online modification of the contribution of past data to account for this drift in user behavior. By learning model updates, our proposed mechanisms, Discount Factor updating and Dynamic Conditional Probability Table assignment, can improve on the prediction accuracy of the best non updating methods on two challenging location-based social networking datasets while remaining robust to the effects of missing check-in data. I

Improving The Performance Of Mobile Phone Crowdsourcing Applications

Mobile phone crowdsourcing is a powerful tool for many types of distributed sensing problems. However, a central issue with this type of system is that it relies on user contributed data, which may be sparse or erroneous. This paper describes our experiences developing a mobile phone crowdsourcing app, Kpark, for monitoring parking availability on a university campus. Our system combines multiple trust-based data fusion techniques to improve the quality of user submitted parking reports and is currently being used by over 1500 students

Multiclass Adaboost Based on an Ensemble of Binary Adaboosts

This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.Open Access</p

Classification with NormalBoost

This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. Since NormalBoost assumes the dataset to be continuous, it is also noise resistant because it only deals with the means and standard deviations of each dimension. Experiments conducted to evaluate its performance shows that NormalBoost performs almost the same as AdaBoost in the classification rate. However, NormalBoost performs 189 times faster than AdaBoost and employs a very little amount of memory when a dataset of 2 million samples with 50 dimensions is invoked

Online Learning Of User-Specific Destination Prediction Models

In this paper, we introduce and evaluate two different mechanisms for efficient online updating of user-specific destination prediction models. Although users can experience long periods of regular behavior during which it is possible to leverage the visitation time to learn a static user-specific model of transportation patterns, many users exhibit a substantial amount of variability in their travel patterns, either because their habits slowly change over time or they oscillate between several different routines. Our methods combat this problem by doing an online modification of the contribution of past data to account for this drift in user behavior. By learning model updates, our proposed mechanisms, Discount Factor updating and Dynamic Conditional Probability Table assignment, can improve on the prediction accuracy of the best non updating methods on two challenging location-based social networking datasets while remaining robust to the effects of missing check-in data. © 2012 IEEE

Traffic Sign Detection Based On Adaboost Color Segmentation And Svm Classification

This paper aims to present a new approach to detect traffic signs which is based on color segmentation using AdaBoost binary classifier and circular Hough Transform. The Adaboost classifier was trained to segment traffic signs images according to the desired color. A voting mechanism was invoked to establish a property curve for each of the candidates. SVM classifier was trained to classify the property curves of each object into their corresponding classes. Experiments conducted on Adaboost color segmentation under different light conditions such as sunny, cloudy, fog and snow fall have showed a performance of 95%. The proposed system was tested on two different groups of traffic signs; the warning and the prohibitory signs. In the case of warning signs, a recognition rate of 98.4% was achieved while it was 97% for prohibitory traffic signs. This test was carried out under a wide range of environmental conditions. © 2013 IEEE