165,624 research outputs found
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
Action Recognition Using Particle Flow Fields
In recent years, research in human action recognition has advanced on multiple fronts to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset), and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow, and interest-points, have been developed for their recognition. Most perform very well in standard action recognition datasets, but fail to produce similar results in more complex, large-scale datasets. Action recognition on large categories of unconstrained videos taken from the web is a very challenging problem compared to datasets like KTH (six actions), IXMAS (thirteen actions), and Weizmann (ten actions). Challenges such as camera motion, different viewpoints, huge interclass variations, cluttered background, occlusions, bad illumination conditions, and poor quality of web videos cause the majority of the state-of-the-art action recognition approaches to fail. An increasing number of categories and the inclusion of actions with high confusion also increase the difficulty of the problem. The approach taken to solve this action recognition problem depends primarily on the dataset and the possibility of detecting and tracking the object of interest. In this dissertation, a new method for video representation is proposed and three new approaches to perform action recognition in different scenarios using varying prerequisites are presented. The prerequisites have decreasing levels of difficulty to obtain: 1) Scenario requires human detection and trackiii ing to perform action recognition; 2) Scenario requires background and foreground separation to perform action recognition; and 3) No pre-processing is required for action recognition. First, we propose a new video representation using optical flow and particle advection. The proposed “Particle Flow Field” (PFF) representation has been used to generate motion descriptors and tested in a Bag of Video Words (BoVW) framework on the KTH dataset. We show that particle flow fields has better performance than other low-level video representations, such as 2D-Gradients, 3D-Gradients and optical flow. Second, we analyze the performance of the state-of-the-art technique based on the histogram of oriented 3D-Gradients in spatio temporal volumes, where human detection and tracking are required. We use the proposed particle flow field and show superior results compared to the histogram of oriented 3D-Gradients in spatio temporal volumes. The proposed method, when used for human action recognition, just needs human detection and does not necessarily require human tracking and figure centric bounding boxes. It has been tested on KTH (six actions), Weizmann (ten actions), and IXMAS (thirteen actions, 4 different views) action recognition datasets. Third, we propose using the scene context information obtained from moving and stationary pixels in the key frames, in conjunction with motion descriptors obtained using Bag of Words framework, to solve the action recognition problem on a large (50 actions) dataset with videos from the web. We perform a combination of early and late fusion on multiple features to handle the huge number of categories. We demonstrate that scene context is a very important feature for performing action recognition on huge datasets. iv The proposed method needs separation of moving and stationary pixels, and does not require any kind of video stabilization, person detection, or tracking and pruning of features. Our approach obtains good performance on a huge number of action categories. It has been tested on the UCF50 dataset with 50 action categories, which is an extension of the UCF YouTube Action (UCF11) Dataset containing 11 action categories. We also tested our approach on the KTH and HMDB51 datasets for comparison. Finally, we focus on solving practice problems in representing actions by bag of spatio temporal features (i.e. cuboids), which has proven valuable for action recognition in recent literature. We observed that the visual vocabulary based (bag of video words) method suffers from many drawbacks in practice, such as: (i) It requires an intensive training stage to obtain good performance; (ii) it is sensitive to the vocabulary size; (iii) it is unable to cope with incremental recognition problems; (iv) it is unable to recognize simultaneous multiple actions; (v) it is unable to perform recognition frame by frame. In order to overcome these drawbacks, we propose a framework to index large scale motion features using Sphere/Rectangle-tree (SR-tree) for incremental action detection and recognition. The recognition comprises of the following two steps: 1) recognizing the local features by non-parametric nearest neighbor (NN), and 2) using a simple voting strategy to label the action. It can also provide localization of the action. Since it does not require feature quantization it can efficiently grow the feature-tree by adding features from new training actions or categories. Our method provides an effective way for practical incremental action recognition. Furthermore, it can handle large scale datasets because the SR-tree is a disk-based v data structure. We tested our approach on two publicly available datasets, the KTH dataset and the IXMAS multi-view dataset, and achieved promising results
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
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