48,066 research outputs found
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
Do-It-Yourself Single Camera 3D Pointer Input Device
We present a new algorithm for single camera 3D reconstruction, or 3D input
for human-computer interfaces, based on precise tracking of an elongated
object, such as a pen, having a pattern of colored bands. To configure the
system, the user provides no more than one labelled image of a handmade
pointer, measurements of its colored bands, and the camera's pinhole projection
matrix. Other systems are of much higher cost and complexity, requiring
combinations of multiple cameras, stereocameras, and pointers with sensors and
lights. Instead of relying on information from multiple devices, we examine our
single view more closely, integrating geometric and appearance constraints to
robustly track the pointer in the presence of occlusion and distractor objects.
By probing objects of known geometry with the pointer, we demonstrate
acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.894244Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and backgroun
Multispectral fingerprinting for improved in vivo cell dynamics analysis
Background:
Tracing cell dynamics in the embryo becomes tremendously difficult when cell trajectories cross in space and time and tissue density obscure individual cell borders. Here, we used the chick neural crest (NC) as a model to test multicolor cell labeling and multispectral confocal imaging strategies to overcome these roadblocks.
Results:
We found that multicolor nuclear cell labeling and multispectral imaging led to improved resolution of in vivo NC cell identification by providing a unique spectral identity for each cell. NC cell spectral identity allowed for more accurate cell tracking and was consistent during short term time-lapse imaging sessions. Computer model simulations predicted significantly better object counting for increasing cell densities in 3-color compared to 1-color nuclear cell labeling. To better resolve cell contacts, we show that a combination of 2-color membrane and 1-color nuclear cell labeling dramatically improved the semi-automated analysis of NC cell interactions, yet preserved the ability to track cell movements. We also found channel versus lambda scanning of multicolor labeled embryos significantly reduced the time and effort of image acquisition and analysis of large 3D volume data sets.
Conclusions:
Our results reveal that multicolor cell labeling and multispectral imaging provide a cellular fingerprint that may uniquely determine a cell's position within the embryo. Together, these methods offer a spectral toolbox to resolve in vivo cell dynamics in unprecedented detail
Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering
This paper presents a new algorithm to track mobile objects in different
scene conditions. The main idea of the proposed tracker includes estimation,
multi-features similarity measures and trajectory filtering. A feature set
(distance, area, shape ratio, color histogram) is defined for each tracked
object to search for the best matching object. Its best matching object and its
state estimated by the Kalman filter are combined to update position and size
of the tracked object. However, the mobile object trajectories are usually
fragmented because of occlusions and misdetections. Therefore, we also propose
a trajectory filtering, named global tracker, aims at removing the noisy
trajectories and fusing the fragmented trajectories belonging to a same mobile
object. The method has been tested with five videos of different scene
conditions. Three of them are provided by the ETISEO benchmarking project
(http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker
performance has been compared with other seven tracking algorithms. The
advantages of our approach over the existing state of the art ones are: (i) no
prior knowledge information is required (e.g. no calibration and no contextual
models are needed), (ii) the tracker is more reliable by combining multiple
feature similarities, (iii) the tracker can perform in different scene
conditions: single/several mobile objects, weak/strong illumination,
indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to
improve the tracker performance, (v) the tracker performance outperforms many
algorithms of the state of the art
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
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