The framework provides a novel pipeline for action recognition. The action recognition task
classifies the action label of the scene. High-speed cameras are commonly used to generate …

Action recognition is an important task for video understanding. Due to expensive time
consumption, the conventional approaches employing the optical flow are difficult to be used …

Recognizing human action in complex video sequences has always been challenging for
researchers due to articulated movements, occlusion, background clutter, and illumination …

Video action recognition is widely applied in video indexing, intelligent surveil-lance,
multimedia understanding, and other fields. Recently, it was greatly improved by …

We investigate video classification via a two-stream convolutional neural network (CNN)
design that directly ingests information extracted from compressed video bitstreams. Our …

This work presents a novel approach to the problem of real-time human action recognition in
intelligent video surveillance. For more efficient and precise labeling of an action, this work …

The two-stream CNNs prove very successful for video-based action recognition. However,
the classical two-stream CNNs are time costly, mainly due to the bottleneck of calculating …

In this paper, we propose an efficient and effective action recognition framework by
combining multiple feature models from dynamic image, optical flow and raw frame, with 3D …

This paper introduces an efficient video representation method based on the dense
trajectory motion map (DTM). We utilize the salient features of dense trajectories and motion …

Convolutional neural networks, which have achieved outstanding performance in image
recognition, have been extensively applied to action recognition. The mainstream …