We propose an effective approach for spatio-temporal action localization in realistic videos.
The approach first detects proposals at the frame-level and scores them with a combination …

Previous work on action recognition has focused on adapting hand-designed local features,
such as SIFT or HOG, from static images to the video domain. In this paper, we propose …

We introduce a new large-scale video dataset designed to assess the performance of
diverse visual event recognition algorithms with a focus on continuous visual event …

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

Automatically recognizing and localizing wide ranges of human actions are crucial for video
understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve …

We propose a multi-region two-stream R-CNN model for action detection in realistic videos.
We start from frame-level action detection based on faster R-CNN, and make three …

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 …

We introduce the concept of dynamic image, a novel compact representation of videos
useful for video analysis, particularly in combination with convolutional neural networks …

Spatio-temporal action detection is an important and challenging problem in video
understanding. The existing action detection benchmarks are limited in aspects of small …

Vision-based human action and activity recognition has an increasing importance among
the computer vision community with applications to visual surveillance, video retrieval and …