116,826 research outputs found
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Automatically recognizing and localizing wide ranges of human actions has
crucial importance for video understanding. Towards this goal, the THUMOS
challenge was introduced in 2013 to serve as a benchmark for action
recognition. Until then, video action recognition, including THUMOS challenge,
had focused primarily on the classification of pre-segmented (i.e., trimmed)
videos, which is an artificial task. In THUMOS 2014, we elevated action
recognition to a more practical level by introducing temporally untrimmed
videos. These also include `background videos' which share similar scenes and
backgrounds as action videos, but are devoid of the specific actions. The three
editions of the challenge organized in 2013--2015 have made THUMOS a common
benchmark for action classification and detection and the annual challenge is
widely attended by teams from around the world.
In this paper we describe the THUMOS benchmark in detail and give an overview
of data collection and annotation procedures. We present the evaluation
protocols used to quantify results in the two THUMOS tasks of action
classification and temporal detection. We also present results of submissions
to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the
differences in action recognition between trimmed and untrimmed videos, and how
well methods trained on trimmed videos generalize to untrimmed videos. We
conclude by proposing several directions and improvements for future THUMOS
challenges.Comment: Preprint submitted to Computer Vision and Image Understandin
Video-based human action recognition using deep learning: a review
Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this led.This work was supported by the Cen-tre d'Etudes et d'Expertise sur les Risques, l'environnement
la mobilité et l'aménagement (CEREMA) and the UC3M Conex-Marie Curie Program.No publicad
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
- …