Multi-class twitter data categorization and geocoding with a novel computing framework

This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed to extract relevant transportation-related information for one week. The SVM classifier achieves \u3e 85% accuracy in identifying transportation-related tweets from structured data. To further categorize the transportation-related tweets into sub-classes: incident, congestion, construction, special events, and other events, three supervised classifiers are used: L-LDA, SVM, and L-LDA incorporated SVM. Findings from this study demonstrate that the analytical framework, which uses the L-LDA incorporated SVM, can classify roadway transportation-related data from Twitter with over 98.3% accuracy, which is significantly higher than the accuracies achieved by standalone L-LDA and SVM

Robust Real-Time Recognition of Action Sequences Using a Multi-Camera Network

Real-time identification of human activities in urban environments is increasingly becoming important in the context of public safety and national security. Distributed camera networks that provide multiple views of a scene are ideally suited for real-time action recognition. However, deployments of multi-camera based real-time action recognition systems have thus far been inhibited because of several practical issues and restrictive assumptions that are typically made such as the knowledge of a subjects orientation with respect to the cameras, the duration of each action and the conformation of a network deployment during the testing phase to that of a training deployment. In reality, action recognition involves classification of continuously streaming data from multiple views which consists of an interleaved sequence of various human actions. While there has been extensive research on machine learning techniques for action recognition from a single view, the issues arising in the fusion of data from multiple views for reliable action recognition have not received as much attention. In this thesis, I have developed a fusion framework for human action recognition using a multi-camera network that addresses these practical issues of unknown subject orientation, unknown view configurations, action interleaving and variable duration actions.;The proposed framework consists of two components: (1) a score-fusion technique that utilizes underlying view-specific supervised learning classifiers to classify an action within a given set of frames and (2) a sliding window technique that is used to parse a sequence of frames into multiple actions. The use of a score-fusion technique as opposed to a feature-level fusion of data from multiple views allows us to robustly classify actions even when camera configurations are arbitrary and different from training phase and at the same time reduces the required network bandwidth for data transmission permitting wireless deployments. Moreover, the proposed framework is independent of the underlying classifier that is used to generate scores for each action snippet and thus offers more flexibility compared to sequential approaches like Hidden Markov Models. The amount of training and parameterization is also significantly lower compared to HMM-based approaches. This Real-Time recognition system has been tested on 4 classifiers which are Linear Discriminant Analysis, Multinomial Naive Bayes, Logistic Regression and Support Vector Machines. Over 90% accuracy has been achieved by this system in Real-Time recognizing variable duration actions performed by the subject. The performance of the system is also shown to be robust to camera failures