Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …

This paper introduces a video representation based on dense trajectories and motion
boundary descriptors. Trajectories capture the local motion information of the video. A dense …

Visual features are of vital importance for human action understanding in videos. This paper
presents a new video representation, called trajectory-pooled deep-convolutional descriptor …

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

Although surveillance video cameras are now widely used, their effectiveness is
questionable. Here, we focus on the challenging task of monitoring crowded events for …

Activity recognition in video is dominated by low-and mid-level features, and while
demonstrably capable, by nature, these features carry little semantic meaning. Inspired by …

Sports data analysis is becoming increasingly large scale, diversified, and shared, but
difficulty persists in rapidly accessing the most crucial information. Previous surveys have …

We address action recognition in videos by modeling the spatial-temporal structures of
human poses. We start by improving a state of the art method for estimating human joint …