28,210 research outputs found
Human action recognition using spatial-temporal analysis.
Masters Degree. University of KwaZulu-Natal, Durban.In the past few decades’ human action recognition (HAR) from video has gained a lot of attention in the computer vision domain. The analysis of human activities in videos span a variety of applications including security and surveillance, entertainment, and the monitoring of the elderly. The task of recognizing human actions in any scenario is a difficult and complex one which is characterized by challenges such as self-occlusion, noisy backgrounds and variations in illumination. However, literature provides various techniques and approaches for action recognition which deal with these challenges. This dissertation focuses on a holistic approach to the human action recognition problem with specific emphasis on spatial-temporal analysis.
Spatial-temporal analysis is achieved by using the Motion History Image (MHI) approach to solve the human action recognition problem. Three variants of MHI are investigated, these are: Original MHI, Modified MHI and Timed MHI. An MHI is a single image describing a silhouettes motion over a period of time. Brighter pixels in the resultant MHI show the most recent movement/motion. One of the key problems of MHI is that it is not easy to know the conditions needed to obtain an MHI silhouette that will result in a high recognition rate for action recognition. These conditions are often neglected and thus pose a problem for human action recognition systems as they could affect their overall performance.
Two methods are proposed to solve the human action recognition problem and to show the conditions needed to obtain high recognition rates using the MHI approach. The first uses the concept of MHI with the Bag of Visual Words (BOVW) approach to recognize human actions. The second approach combines MHI with Local Binary Patterns (LBP). The Weizmann and KTH datasets are then used to validate the proposed methods.
Results from experiments show promising recognition rates when compared to some existing methods. The BOVW approach used in combination with the three variants of MHI achieved the highest recognition rates compared to the LBP method. The original MHI method resulted in the highest recognition rate of 87% on the Weizmann dataset and an 81.6% recognition rate is achieved on the KTH dataset using the Modified MHI approach
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
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
3D action recognition has broad applications in human-computer interaction
and intelligent surveillance. However, recognizing similar actions remains
challenging since previous literature fails to capture motion and shape cues
effectively from noisy depth data. In this paper, we propose a novel two-layer
Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and
jointly encodes both motion and shape cues. First, background clutter is
removed by a background modeling method that is designed for depth data. Then,
motion and shape cues are jointly used to generate robust and distinctive
spatial-temporal interest points (STIPs): motion-based STIPs and shape-based
STIPs. In the first layer of our model, a multi-scale 3D local steering kernel
(M3DLSK) descriptor is proposed to describe local appearances of cuboids around
motion-based STIPs. In the second layer, a spatial-temporal vector (STV)
descriptor is proposed to describe the spatial-temporal distributions of
shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape
cues are combined to form a fused action representation. Our model performs
favorably compared with common STIP detection and description methods. Thorough
experiments verify that our model is effective in distinguishing similar
actions and robust to background clutter, partial occlusions and pepper noise
A robust and efficient video representation for action recognition
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 by explicit camera motion estimation. More
specifically, we extract feature point matches between frames using SURF
descriptors and dense optical flow. The matches are used to estimate a
homography with RANSAC. To improve the robustness of homography estimation, a
human detector is employed to remove outlier matches from the human body as
human motion is not constrained by the camera. Trajectories consistent with the
homography are considered as due to camera motion, and thus removed. We also
use the homography to cancel out camera motion from the optical flow. This
results in significant improvement on motion-based HOF and MBH descriptors. We
further explore the recent Fisher vector as an alternative feature encoding
approach to the standard bag-of-words histogram, and consider different ways to
include spatial layout information in these encodings. We present a large and
varied set of evaluations, considering (i) classification of short basic
actions on six datasets, (ii) localization of such actions in feature-length
movies, and (iii) large-scale recognition of complex events. We find that our
improved trajectory features significantly outperform previous dense
trajectories, and that Fisher vectors are superior to bag-of-words encodings
for video recognition tasks. In all three tasks, we show substantial
improvements over the state-of-the-art results
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect skeletons for 3D human action recognition. Firstly, the orientations of
limbs connected by two skeleton joints are computed and each orientation is
encoded into one of the 27 states indicating the spatial relationship of the
joints. Secondly, limbs are combined into parts and the limb's states are
mapped into part states. Finally, frequent pattern mining is employed to mine
the most frequent and relevant (discriminative, representative and
non-redundant) states of parts in continuous several frames. These parts are
referred to as Frequent Local Parts or FLPs. The FLPs allow us to build
powerful bag-of-FLP-based action representation. This new representation yields
state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D
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