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 …

This paper presents an overview of state-of-the-art methods in activity recognition using
semantic features. Unlike low-level features, semantic features describe inherent …

In spite of many dataset efforts for human action recognition, current computer vision
algorithms are still severely limited in terms of the variability and complexity of the actions …

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 …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

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 …

Action recognition on large categories of unconstrained videos taken from the web is a very
challenging problem compared to datasets like KTH (6 actions), IXMAS (13 actions), and …

Abstract Representation of video is a vital problem in action recognition. This paper
proposes Stacked Fisher Vectors (SFV), a new representation with multi-layer nested Fisher …

Human activity recognition has potential to impact a wide range of applications from
surveillance to human computer interfaces to content based video retrieval. Recently, the …

This paper introduces a state-of-the-art video representation and applies it to efficient action
recognition and detection. We first propose to improve the popular dense trajectory features …