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arXiv2017

Extraction and Classification of Diving Clips from Continuous Video Footage

Due to recent advances in technology, the recording and analysis of video data has become an increasingly common component of athlete training programmes. Today it is incredibly easy and affordable to set up a fixed camera and record athletes in a wide range of sports, such as diving, gymnastics, golf, tennis, etc. However, the manual analysis of the obtained footage is a time-consuming task which involves isolating actions of interest and categorizing them using domain-specific knowledge. In order to automate this kind of task, three challenging sub-problems are often encountered: 1) temporally cropping events/actions of interest from continuous video; 2) tracking the object of interest; and 3) classifying the events/actions of interest. Most previous work has focused on solving just one of the above sub-problems in isolation. In contrast, this paper provides a complete solution to the overall action monitoring task in the context of a challenging real-world exemplar. Specifically, we address the problem of diving classification. This is a challenging problem since the person (diver) of interest typically occupies fewer than 1% of the pixels in each frame. The model is required to learn the temporal boundaries of a dive, even though other divers and bystanders may be in view. Finally, the model must be sensitive to subtle changes in body pose over a large number of frames to determine the classification code. We provide effective solutions to each of the sub-problems which combine to provide a highly functional solution to the task as a whole. The techniques proposed can be easily generalized to video footage recorded from other sports.

cs.CV↗

arXiv2017

Evaluating the Dynamic Behavior of Database Applications

This paper explores the effect that changing access patterns has on the performance of database management systems. Changes in access patterns play an important role in determining the efficiency of key performance optimization techniques, such as dynamic clustering, prefetching, and buffer replacement. However, all existing benchmarks or evaluation frameworks produce static access patterns in which objects are always accessed in the same order repeatedly. Hence, we have proposed the Dynamic Evaluation Framework (DEF) that simulates access pattern changes using configurable styles of change. DEF has been designed to be open and fully extensible (e.g., new access pattern change models can be added easily). In this paper, we instantiate DEF into the Dynamic object Evaluation Framework (DoEF) which is designed for object databases, i.e., object-oriented or object-relational databases such as multi-media databases or most XML databases.The capabilities of DoEF have been evaluated by simulating the execution of four different dynamic clustering algorithms. The results confirm our analysis that flexible conservative re-clustering is the key in determining a clustering algorithm's ability to adapt to changes in access pattern. These results show the effectiveness of DoEF at determining the adaptability of each dynamic clustering algorithm to changes in access pattern in a simulation environment. In a second set of experiments, we have used DoEF to compare the performance of two real-life object stores : Platypus and SHORE. DoEF has helped to reveal the poor swapping performance of Platypus.

cs.DB↗

arXiv2012

Boosting Moving Object Indexing through Velocity Partitioning

There have been intense research interests in moving object indexing in the past decade. However, existing work did not exploit the important property of skewed velocity distributions. In many real world scenarios, objects travel predominantly along only a few directions. Examples include vehicles on road networks, flights, people walking on the streets, etc. The search space for a query is heavily dependent on the velocity distribution of the objects grouped in the nodes of an index tree. Motivated by this observation, we propose the velocity partitioning (VP) technique, which exploits the skew in velocity distribution to speed up query processing using moving object indexes. The VP technique first identifies the "dominant velocity axes (DVAs)" using a combination of principal components analysis (PCA) and k-means clustering. Then, a moving object index (e.g., a TPR-tree) is created based on each DVA, using the DVA as an axis of the underlying coordinate system. An object is maintained in the index whose DVA is closest to the object's current moving direction. Thus, all the objects in an index are moving in a near 1-dimensional space instead of a 2-dimensional space. As a result, the expansion of the search space with time is greatly reduced, from a quadratic function of the maximum speed (of the objects in the search range) to a near linear function of the maximum speed. The VP technique can be applied to a wide range of moving object index structures. We have implemented the VP technique on two representative ones, the TPR*-tree and the Bx-tree. Extensive experiments validate that the VP technique consistently improves the performance of those index structures.

cs.DB↗

arXiv2007

DOEF: A Dynamic Object Evaluation Framework

In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques like clustering have been evaluated up to now. This paper opens up research regarding this issue by proposing a dynamic object evaluation framework (DOEF) that accomplishes access pattern change by defining configurable styles of change. This preliminary prototype has been designed to be open and fully extensible. To illustrate the capabilities of DOEF, we used it to compare the performances of four state of the art dynamic clustering algorithms. The results show that DOEF is indeed effective at determining the adaptability of each dynamic clustering algorithm to changes in access pattern.

cs.DB↗

arXiv2007

Une plate-forme dynamique pour l'évaluation des performances des bases de données à objets

In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques such as clustering have been evaluated up to now. This paper opens up research regarding this issue by proposing a dynamic object evaluation framework (DOEF). DOEF accomplishes access pattern change by defining configurable styles of change. It is a preliminary prototype that has been designed to be open and fully extensible. Though originally designed for the object-oriented model, it can also be used within the object-relational model with few adaptations. Furthermore, new access pattern change models can be added too. To illustrate the capabilities of DOEF, we conducted two different sets of experiments. In the first set of experiments, we used DOEF to compare the performances of four state of the art dynamic clustering algorithms. The results show that DOEF is effective at determining the adaptability of each dynamic clustering algorithm to changes in access pattern. They also led us to conclude that dynamic clustering algorithms can cope with moderate levels of access pattern change, but that performance rapidly degrades to be worse than no clustering when vigorous styles of access pattern change are applied. In the second set of experiments, we used DOEF to compare the performance of two different object stores: Platypus and SHORE. The use of DOEF exposed the poor swapping performance of Platypus.

cs.DB↗