Action Recognition with Actons

With the improved accessibility to an exploding amoun-t of video data and growing demands in a wide range of video analysis applications, video-based action recogni-tion/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level “acton ” representation. The weakly-supervised actons are learned via a new max-margin multi-channel multiple instance learning framework, which can capture multiple mid-level action concepts simultaneously. The learned actons (with no requirement for detailed manual annotations) observe the properties of being compact, infor-mative, discriminative, and easy to scale. The experimental results demonstrate the effectiveness of applying the learned actons in our two-layer structure, and show the state-of-the-art recognition performance on two challenging action datasets, i.e., Youtube and HMDB51. 1

Surveillance video summarization based on trajectory rarity measure

The dynamic video summarization of surveillance videos has several critical applications, mainly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, buildings, schools, hospitals, roads, among others. This study presents an approach for the generation of dynamic summary on surveillance video domain based on human trajectories. It has an emphasis on trajectory descriptors in conjunction with the unsupervised clustering method. Our approach contribute to existing literature concerning the combination of methods and objectives. We hypothesize that the clustering of trajectories permits to identify rare trajectories base on their morphology. The clustering as an output provides numerous subsets of trajectories or clusters and the number of elements of a specific cluster is used to determine their rarity. Those subsets with few components are rare while the others that have a high number of elements are considered ordinary; therefore, the implications of our study show that is possible to use unsupervised clustering for automatic detection of rare trajectories based on their morphology and with this information segment videos. We experimented with different sets of trajectories segmenting the rare videos from our ground truth.Trabajo de investigació