2,631 research outputs found
Anomaly Detection, Rule Adaptation and Rule Induction Methodologies in the Context of Automated Sports Video Annotation.
Automated video annotation is a topic of considerable interest in computer vision due to its applications in video search, object based video encoding and enhanced broadcast content. The domain of sport broadcasting is, in particular, the subject of current research attention due to its fixed, rule governed, content. This research work aims to develop, analyze and demonstrate novel methodologies that can be useful in the context of adaptive and automated video annotation systems. In this thesis, we present methodologies for addressing the problems of anomaly detection, rule adaptation and rule induction for court based sports such as tennis and badminton. We first introduce an HMM induction strategy for a court-model based method that uses the court structure in the form of a lattice for two related modalities of singles and doubles tennis to tackle the problems of anomaly detection and rectification. We also introduce another anomaly detection methodology that is based on the disparity between the low-level vision based classifiers and the high-level contextual classifier. Another approach to address the problem of rule adaptation is also proposed that employs Convex hulling of the anomalous states. We also investigate a number of novel hierarchical HMM generating methods for stochastic induction of game rules. These methodologies include, Cartesian product Label-based Hierarchical Bottom-up Clustering (CLHBC) that employs prior information within the label structures. A new constrained variant of the classical Chinese Restaurant Process (CRP) is also introduced that is relevant to sports games. We also propose two hybrid methodologies in this context and a comparative analysis is made against the flat Markov model. We also show that these methods are also generalizable to other rule based environments
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management
(CIKM) 201
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Contextual Model-Based Collaborative Filtering for Recommender Systems
Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore that user preferences can change according to context, resulting in recommendations that do not fit user interests. Context-aware models have been proposed to address this issue, but these models have problems of their own. The ever-increasing speed at which data are generated presents a scalability challenge for single-model approaches. Moreover, the complexity of these models prevents small players from adapting and implementing contextual models that meet their needs.
This thesis addresses these issues by proposing the (CF)2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering (CF) models. CF has been available for decades, and its methods and benefits have been extensively discussed and implemented. Moreover, the use of context as filtering criteria for local learning addresses the scalability issues caused by the use of large datasets. Therefore, the proposed architecture enables the creation of contextual recommendations using several models instead of one, with each model representing a context. In addition, the architecture is implemented and evaluated in two case studies. Results show that contextual models trained with a small fraction of the data resulted in similar or better accuracy compared to CF models trained with the total dataset. Moreover, experiments indicate that local learning using contextual information outperforms random selection in accuracy and in training time
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