A qualitative approach to the identification, visualisation and interpretation of repetitive motion patterns in groups of moving point objects

Discovering repetitive patterns is important in a wide range of research areas, such as bioinformatics and human movement analysis. This study puts forward a new methodology to identify, visualise and interpret repetitive motion patterns in groups of Moving Point Objects (MPOs). The methodology consists of three steps. First, motion patterns are qualitatively described using the Qualitative Trajectory Calculus (QTC). Second, a similarity analysis is conducted to compare motion patterns and identify repetitive patterns. Third, repetitive motion patterns are represented and interpreted in a continuous triangular model. As an illustration of the usefulness of combining these hitherto separated methods, a specific movement case is examined: Samba dance, a rhythmical dance will? many repetitive movements. The results show that the presented methodology is able to successfully identify, visualize and interpret the contained repetitive motions

Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh

Assessing similarity of dynamic geographic phenomena in spatiotemporal databases.

The growing availability of routine observations from satellite imagery and other remote sensors holds great promise for improved understanding of processes that act in the landscape. However, geographers' ability to effectively use such spatiotemporal data is challenged by large data volume and limitations of conventional data models in geographic information systems (GIS), which provide limited support for querying and exploration of spatiotemporal data other than simple comparisons of temporally referenced snapshots. Current GIS representations allow measurement of change but do not address coherent patterns of change that reflects the working of geographic events and processes. This dissertation presents a representational and query framework to overcome the limitations and enable assessing similarity of dynamic phenomena. The research includes three self contained but related studies: (1) development of a representational framework that incorporates spatiotemporal properties of geographic phenomena, (2) development of a framework to characterize events and processes that can be inferred from GIS databases, and (3) development of a method to assess similarity of events and processes based on the temporal sequences of spatiotemporal properties. Collectively the studies contribute to scientific understanding of spatiotemporal components of geographic processes and technological advances in representation and analysis

Computing Similarity between a Pair of Trajectories

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling

Natural language querying for video databases

Cataloged from PDF version of article.The video databases have become popular in various areas due to the recent advances in technology. Video archive systems need user-friendly interfaces to retrieve video frames. In this paper, a user interface based on natural language processing (NLP) to a video database system is described. The video database is based on a content-based spatio-temporal video data model. The data model is focused on the semantic content which includes objects, activities, and spatial properties of objects. Spatio-temporal relationships between video objects and also trajectories of moving objects can be queried with this data model. In this video database system, a natural language interface enables flexible querying. The queries, which are given as English sentences, are parsed using link parser. The semantic representations of the queries are extracted from their syntactic structures using information extraction techniques. The extracted semantic representations are used to call the related parts of the underlying video database system to return the results of the queries. Not only exact matches but similar objects and activities are also returned from the database with the help of the conceptual ontology module. This module is implemented using a distance-based method of semantic similarity search on the semantic domain-independent ontology, WordNet. (C) 2008 Elsevier Inc. All rights reserved