14802063
Minimal-latency human action recognition using reliable-inference
We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information.
10444578
A reliable-inference framework for recognition of human actions
We present an action recognition method based on the concept of reliable inference. Our approach is formulated in a probabilistic framework using posterior class ratios to verify the saliency of an input before committing to any action classification. The framework is evaluated in the context of walking, running, and standing at multiple viewpoints and compared to ML and MAP approaches. Results examining individual silhouette images with the framework demonstrate that these actions can be reliably discriminated while discounting confusing images.
9994863
Motion segmentation and pose recognition with motion history gradients
Abstract. This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program.
10341811
A Two-Stage Template Approach to Person Detection in Thermal Imagery
We present a two-stage template-based method to detect people in widely varying thermal imagery. The approach initially performs a fast screening procedure using a generalized template to locate potential person locations. Next an AdaBoosted ensemble classifier using automatically tuned filters is employed to test the hypothesized person locations. We demonstrate and evaluate the approach using a challenging dataset of thermal imagery
10674834
Visual Categorization of Children and Adult Walking Styles
We present an approach for visual discrimination of children from adults in video using characteristic regularities present in their locomotion patterns. The framework employs computer vision to analyze correlated, scale invariant locomotion properties for classifying different styles of walking. Male and female subjects for the experiments include six children (3-5 yrs) and nine adults (30-52 yrs). For the analysis, we coordinate a minimalist point-representation of the human body with a space-time analysis of head and ankle trajectories to characterize the modality. Together the properties of relative stride length and stride frequency are shown to clearly differentiate children from adult walkers. The highly correlated log-linear relationships for the stride properties are exploited to reduce the categorization problem to a linear discrimination task. Using a trained two-class linear perceptron, we were able to achieve a correct classification rate of 93-95% on our dataset. Our approach emphasizing the natural modal behavior in human motion offers a useful and general methodology as the basis for designing efficient motion recognition systems using limited visual features.
16142452
Differential video coding of face and gesture events in presentation videos
Currently, bandwidth limitations pose a major challenge for delivering high-quality multimedia information over the Internet to users. In this research, we aim to provide a better compression of presentation videos (e.g., lectures). The approach is based on the idea that people tend to pay more attention to the face and gesturing hands, and therefore these regions are given more resolution than the remaining image. Our method first detects and tracks the face and hand regions using color-based segmentation and Kalman filtering. Next, different classes of natural hand gesture are recognized from the hand trajectories by identifying gesture holds, position/velocity changes, and repetitive movements. The detected face/ hand regions and gesture events in the video are then encoded at higher resolution than the remaining lower-resolution background. We present results of the tracking and gesture recognition approach, and evaluate and compare videos compressed with the proposed method to uniform compression.
14742091
On local pruning of association rules using directed hypergraphs
Here we propose an adaptive local pruning method for association rules. Our method exploits the exact mapping between a certain class of association rules, namely those whose consequents are singletons and backward directed hypergraphs (B-graphs). The hypergraph which represents the association rules is called an association rules network(ARN). Here we present a simple example of an ARN. We further prove several properties of the ARN and apply the results of our approach to two popular data sets.
27293359
An Integrated Approach to Learning Bayesian Networks of Rules
Inductive Logic Programming (ILP) is a popular approach for learning rules for classification tasks. An important question is how to combine the individual rules to obtain a useful classifier. In some instances, converting each learned rule into a binary feature for a Bayes net learner improves the accuracy compared to the standard decision list approach [3,4,14]. This results in a two-step process, where rules are generated in the first phase, and the classifier is learned in the second phase. We propose an algorithm that interleaves the two steps, by incrementally building a Bayes net during rule learning. Each candidate rule is introduced into the network, and scored by whether it improves the performance of the classifier. We call the algorithm SAYU for Score As You Use. We evaluate two structure learning algorithms Naive Bayes and Tree Augmented Naive Bayes. We test SAYU on four different datasets and see a significant improvement in two out of the four applications. Furthermore, the theories that SAYU learns tend to consist of far fewer rules than the theories in the two-step approach.
11683980
Qualitative interpretation of sensor patterns
A framework that provides the ability to generate qualitative interpretations (QIs) from multisensor trend patterns for monitoring, control, and optimization of chemical plants is presented. QIs are transformations of sensor and quality product data into useful symbolic abstractions. The framework is founded on the principles of similarity-based pattern recognition. Although demonstrated for normality identification, the machine methodology is general-purpose and applicable to any context-dependent QI problem. The objective of this approach is to create a QI-map of known pattern classes that consists of spatially distinguishable regions of patterns in an n-dimensional representation space. Creation of a QI-map is a two-step process: unsupervised map generation followed by supervised labeling. The application of the ART2 neural network for clustering in the QI-map is described. The application of the ART2-based QI-map approach to process monitoring of a recycle reactor is also described.<<ETX>>
3804028
Household indicators: design to inform and engage citizens
Urban simulation systems can be a powerful tool for helping to understand the complex, long-term consequences of urban planning decisions. Simulation results are summarized and reported using indicators, aggregate measures such as population density or total minutes of vehicle delay. To citizens, these indicators may seem abstract and unfamiliar. This extended abstract presents design work in progress on Household Indicators, a new form of indicator designed especially for citizens. Accessed through a web-based interface, Household Indicators are intended to inform citizens by relating simulation results to citizens' life experiences, and to engage citizens by addressing the question, "How will this decision affect me?.
19186309
Using an Aligned Ontology to Process User Queries
Ontologies have been developed for a number of knowledge domains as diverse as clinical terminology, photo camera parts and micro-array gene expression data. However, processing user queries over a set of overlapping ontologies is not straightforward because they have often been created by independent groups of expertise, each of them adopting different configurations for ontology concepts. A project being carried out at the Brazilian Corporation of Agricultural Research has produced ontologies in sub-domains such as beef cattle, dairy cattle, sheep and beans, among others. This paper focuses on an alignment method for these ontologies based on Formal Concept Analysis, a data analysis technique founded on lattice theory, and a strategy for processing user queries.