5,199 research outputs found
MetaSpace II: Object and full-body tracking for interaction and navigation in social VR
MetaSpace II (MS2) is a social Virtual Reality (VR) system where multiple
users can not only see and hear but also interact with each other, grasp and
manipulate objects, walk around in space, and get tactile feedback. MS2 allows
walking in physical space by tracking each user's skeleton in real-time and
allows users to feel by employing passive haptics i.e., when users touch or
manipulate an object in the virtual world, they simultaneously also touch or
manipulate a corresponding object in the physical world. To enable these
elements in VR, MS2 creates a correspondence in spatial layout and object
placement by building the virtual world on top of a 3D scan of the real world.
Through the association between the real and virtual world, users are able to
walk freely while wearing a head-mounted device, avoid obstacles like walls and
furniture, and interact with people and objects. Most current virtual reality
(VR) environments are designed for a single user experience where interactions
with virtual objects are mediated by hand-held input devices or hand gestures.
Additionally, users are only shown a representation of their hands in VR
floating in front of the camera as seen from a first person perspective. We
believe, representing each user as a full-body avatar that is controlled by
natural movements of the person in the real world (see Figure 1d), can greatly
enhance believability and a user's sense immersion in VR.Comment: 10 pages, 9 figures. Video:
http://living.media.mit.edu/projects/metaspace-ii
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
Articulated human tracking and behavioural analysis in video sequences
Recently, there has been a dramatic growth of interest in the observation and tracking
of human subjects through video sequences. Arguably, the principal impetus has come
from the perceived demand for technological surveillance, however applications in entertainment,
intelligent domiciles and medicine are also increasing. This thesis examines
human articulated tracking and the classi cation of human movement, rst separately
and then as a sequential process.
First, this thesis considers the development and training of a 3D model of human body
structure and dynamics. To process video sequences, an observation model is also designed
with a multi-component likelihood based on edge, silhouette and colour. This is de ned on
the articulated limbs, and visible from a single or multiple cameras, each of which may be
calibrated from that sequence. Second, for behavioural analysis, we develop a methodology
in which actions and activities are described by semantic labels generated from a Movement
Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was
developed for human tracking that allows multi-level parameter search consistent with the
body structure. This tracker relies on the articulated motion prediction provided by the
MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to
generate a probabilistic activity description with action labels.
The implemented algorithms for tracking and behavioural analysis are tested extensively
and independently against ground truth on human tracking and surveillance
datasets. Dynamic models are shown to predict and generate synthetic motion, while
MCM recovers both periodic and non-periodic activities, de ned either on the whole body
or at the limb level. Tracking results are comparable with the state of the art, however
the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS
3D Simulation-based Analysis of Individual and Group Dynamic Behaviour in Video Surveillance
The visual behaviour analysis of individual and group dynamics is a subject of extensive research in both academia and industry. However, despite the recent technological advancements, the problem remains difficult. Most of the approaches concentrate on direct extraction and classification of graphical features from the video feed, analysing the behaviour directly from the source. The major obstacle, which impacts the real-time performance, is the necessity of combining processing of enormous volume of video data with complex symbolic data analysis. In this paper, we present the results of the experimental validation of a new method for dynamic behaviour analysis in visual analytics framework, which has as a core an agent-based, event-driven simulator. Our method utilizes only limited data extracted from the live video to analyse the activities monitored by surveillance cameras. Through combining the ontology of the visual scene, which accounts for the logical features of the observed world, with the patterns of dynamic behaviour, approximating the visual dynamics of the world, the framework allows recognizing the behaviour patterns on the basis of logical events rather than on physical appearance. This approach has several advantages. Firstly, the simulation reduces the complexity of data processing by eliminating the need of precise graphic data. Secondly, the granularity and precision of the analysed behaviour patterns can be controlled by parameters of the simulation itself. The experiments prove in a convincing manner that the simulation generates rich enough data to analyse the dynamic behaviour in real time with sufficient precision, completely adequate for many applications of video surveillance
Robust pedestrian detection and tracking in crowded scenes
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases
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