Video-based human action recognition is currently one of the most active research areas in
computer vision. Various research studies indicate that the performance of action …

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

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

While large datasets have proven to be a key enabler for progress in computer vision, they
can have biases that lead to erroneous conclusions. The notion of the representation bias of …

Current person re-identification (re-id) methods typically rely on single-frame imagery
features, and ignore space-time information from image sequences. Single-frame (single …

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 …

Vision-based human action recognition is the process of labeling image sequences with
action labels. Robust solutions to this problem have applications in domains such as visual …

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 …

Human activity recognition is an important area of computer vision research. Its applications
include surveillance systems, patient monitoring systems, and a variety of systems that …