10,190 research outputs found
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.Comment: 16 pages and 7 table
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
Histogram of Oriented Principal Components for Cross-View Action Recognition
Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which are viewpoint
dependent. In contrast, we directly process pointclouds for cross-view action
recognition from unknown and unseen views. We propose the Histogram of Oriented
Principal Components (HOPC) descriptor that is robust to noise, viewpoint,
scale and action speed variations. At a 3D point, HOPC is computed by
projecting the three scaled eigenvectors of the pointcloud within its local
spatio-temporal support volume onto the vertices of a regular dodecahedron.
HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D
pointcloud sequences so that view-invariant STK descriptors (or Local HOPC
descriptors) at these key locations only are used for action recognition. We
also propose a global descriptor computed from the normalized spatio-temporal
distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the
performance of our proposed descriptors against nine existing techniques on two
cross-view and three single-view human action recognition datasets. The
Experimental results show that our techniques provide significant improvement
over state-of-the-art methods
Multimodal Multipart Learning for Action Recognition in Depth Videos
The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes the structured sparsity to model each action as a combination of
multimodal features from a sparse set of body parts. To represent dynamics and
appearance of parts, we employ a heterogeneous set of depth and skeleton based
features. The proper structure of multimodal multipart features are formulated
into the learning framework via the proposed hierarchical mixed norm, to
regularize the structured features of each part and to apply sparsity between
them, in favor of a group feature selection. Our experimental results expose
the effectiveness of the proposed learning method in which it outperforms other
methods in all three tested datasets while saturating one of them by achieving
perfect accuracy
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
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Recognizing human activity using RGBD data
textTraditional computer vision algorithms try to understand the world using visible light cameras. However, there are inherent limitations of this type of data source. First, visible light images are sensitive to illumination changes and background clutter. Second, the 3D structural information of the scene is lost when projecting the 3D world to 2D images. Recovering the 3D information from 2D images is a challenging problem. Range sensors have existed for over thirty years, which capture 3D characteristics of the scene. However, earlier range sensors were either too expensive, difficult to use in human environments, slow at acquiring data, or provided a poor estimation of distance. Recently, the easy access to the RGBD data at real-time frame rate is leading to a revolution in perception and inspired many new research using RGBD data. I propose algorithms to detect persons and understand the activities using RGBD data. I demonstrate the solutions to many computer vision problems may be improved with the added depth channel. The 3D structural information may give rise to algorithms with real-time and view-invariant properties in a faster and easier fashion. When both data sources are available, the features extracted from the depth channel may be combined with traditional features computed from RGB channels to generate more robust systems with enhanced recognition abilities, which may be able to deal with more challenging scenarios. As a starting point, the first problem is to find the persons of various poses in the scene, including moving or static persons. Localizing humans from RGB images is limited by the lighting conditions and background clutter. Depth image gives alternative ways to find the humans in the scene. In the past, detection of humans from range data is usually achieved by tracking, which does not work for indoor person detection. In this thesis, I propose a model based approach to detect the persons using the structural information embedded in the depth image. I propose a 2D head contour model and a 3D head surface model to look for the head-shoulder part of the person. Then, a segmentation scheme is proposed to segment the full human body from the background and extract the contour. I also give a tracking algorithm based on the detection result. I further research on recognizing human actions and activities. I propose two features for recognizing human activities. The first feature is drawn from the skeletal joint locations estimated from a depth image. It is a compact representation of the human posture called histograms of 3D joint locations (HOJ3D). This representation is view-invariant and the whole algorithm runs at real-time. This feature may benefit many applications to get a fast estimation of the posture and action of the human subject. The second feature is a spatio-temporal feature for depth video, which is called Depth Cuboid Similarity Feature (DCSF). The interest points are extracted using an algorithm that effectively suppresses the noise and finds salient human motions. DCSF is extracted centered on each interest point, which forms the description of the video contents. This descriptor can be used to recognize the activities with no dependence on skeleton information or pre-processing steps such as motion segmentation, tracking, or even image de-noising or hole-filling. It is more flexible and widely applicable to many scenarios. Finally, all the features herein developed are combined to solve a novel problem: first-person human activity recognition using RGBD data. Traditional activity recognition algorithms focus on recognizing activities from a third-person perspective. I propose to recognize activities from a first-person perspective with RGBD data. This task is very novel and extremely challenging due to the large amount of camera motion either due to self exploration or the response of the interaction. I extracted 3D optical flow features as the motion descriptor, 3D skeletal joints features as posture descriptors, spatio-temporal features as local appearance descriptors to describe the first-person videos. To address the ego-motion of the camera, I propose an attention mask to guide the recognition procedures and separate the features on the ego-motion region and independent-motion region. The 3D features are very useful at summarizing the discerning information of the activities. In addition, the combination of the 3D features with existing 2D features brings more robust recognition results and make the algorithm capable of dealing with more challenging cases.Electrical and Computer Engineerin
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