5 research outputs found
DIY Human Action Data Set Generation
The recent successes in applying deep learning techniques to solve standard
computer vision problems has aspired researchers to propose new computer vision
problems in different domains. As previously established in the field, training
data itself plays a significant role in the machine learning process,
especially deep learning approaches which are data hungry. In order to solve
each new problem and get a decent performance, a large amount of data needs to
be captured which may in many cases pose logistical difficulties. Therefore,
the ability to generate de novo data or expand an existing data set, however
small, in order to satisfy data requirement of current networks may be
invaluable. Herein, we introduce a novel way to partition an action video clip
into action, subject and context. Each part is manipulated separately and
reassembled with our proposed video generation technique. Furthermore, our
novel human skeleton trajectory generation along with our proposed video
generation technique, enables us to generate unlimited action recognition
training data. These techniques enables us to generate video action clips from
an small set without costly and time-consuming data acquisition. Lastly, we
prove through extensive set of experiments on two small human action
recognition data sets, that this new data generation technique can improve the
performance of current action recognition neural nets
Discovering Human Interactions in Videos with Limited Data Labeling
We present a novel approach for discovering human interactions in videos. Activity understanding techniques usually require a large number of labeled examples, which are not available in many practical cases. Here, we focus on recovering semantically meaningful clusters of human-human and human-object interaction in an unsupervised fashion. A new iterative solution is introduced based on Maximum Margin Clustering (MMC), which also accepts user feedback to refine clusters. This is achieved by formulating the whole process as a unified constrained latent max-margin clustering problem. Extensive experiments have been carried out over three challenging datasets, Collective Activity, VIRAT, and UT-interaction. Empirical results demonstrate that the proposed algorithm can efficiently discover perfect semantic clusters of human interactions with only a small amount of labeling effort