5,201 research outputs found
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages
with 9 figure
Automated Tracking and Estimation for Control of Non-rigid Cloth
This report is a summary of research conducted on cloth tracking for
automated textile manufacturing during a two semester long research course at
Georgia Tech. This work was completed in 2009. Advances in current sensing
technology such as the Microsoft Kinect would now allow me to relax certain
assumptions and generally improve the tracking performance. This is because a
major part of my approach described in this paper was to track features in a 2D
image and use these to estimate the cloth deformation. Innovations such as the
Kinect would improve estimation due to the automatic depth information obtained
when tracking 2D pixel locations. Additionally, higher resolution camera images
would probably give better quality feature tracking. However, although I would
use different technology now to implement this tracker, the algorithm described
and implemented in this paper is still a viable approach which is why I am
publishing this as a tech report for reference. In addition, although the
related work is a bit exhaustive, it will be useful to a reader who is new to
methods for tracking and estimation as well as modeling of cloth
Stereo Vision for Unmanned Aerial VehicleDetection, Tracking, and Motion Control
An innovative method of detecting Unmanned Aerial Vehicles (UAVs) is
presented. The goal of this study is to develop a robust setup for an
autonomous multi-rotor hunter UAV, capable of visually detecting and tracking
the intruder UAVs for real-time motion planning. The system consists of two
parts: object detection using a stereo camera to generate 3D point cloud data
and video tracking applying a Kalman filter for UAV motion modeling. After
detection, the hunter can aim and shoot a tethered net at the intruder to
neutralize it. The computer vision, motion tracking, and planning algorithms
can be implemented on a portable computer installed on the hunter UAV.Comment: This work was accepted as a Late-Breaking result at the IFAC World
Congress 202
Implementation of an Onboard Visual Tracking System with Small Unmanned Aerial Vehicle (UAV)
This paper presents a visual tracking system that is capable or running real
time on-board a small UAV (Unmanned Aerial Vehicle). The tracking system is
computationally efficient and invariant to lighting changes and rotation of the
object or the camera. Detection and tracking is autonomously carried out on the
payload computer and there are two different methods for creation of the image
patches. The first method starts detecting and tracking using a stored image
patch created prior to flight with previous flight data. The second method
allows the operator on the ground to select the interest object for the UAV to
track. The tracking system is capable of re-detecting the object of interest in
the events of tracking failure. Performance of the tracking system was verified
both in the lab and during actual flights of the UAV. Results show that the
system can run on-board and track a diverse set of objects in real time.Comment: 9 pages; 6 figures; International Journal of Innovative Technology
and Creative Engineering (ISSN:2045-8711) VOl.1 No. 10 OCTOBER 201
Object Detection by Spatio-Temporal Analysis and Tracking of the Detected Objects in a Video with Variable Background
In this paper we propose a novel approach for detecting and tracking objects
in videos with variable background i.e. videos captured by moving cameras
without any additional sensor. In a video captured by a moving camera, both the
background and foreground are changing in each frame of the image sequence. So
for these videos, modeling a single background with traditional background
modeling methods is infeasible and thus the detection of actual moving object
in a variable background is a challenging task. To detect actual moving object
in this work, spatio-temporal blobs have been generated in each frame by
spatio-temporal analysis of the image sequence using a three-dimensional Gabor
filter. Then individual blobs, which are parts of one object are merged using
Minimum Spanning Tree to form the moving object in the variable background. The
height, width and four-bin gray-value histogram of the object are calculated as
its features and an object is tracked in each frame using these features to
generate the trajectories of the object through the video sequence. In this
work, problem of data association during tracking is solved by Linear
Assignment Problem and occlusion is handled by the application of kalman
filter. The major advantage of our method over most of the existing tracking
algorithms is that, the proposed method does not require initialization in the
first frame or training on sample data to perform. Performance of the algorithm
has been tested on benchmark videos and very satisfactory result has been
achieved. The performance of the algorithm is also comparable and superior with
respect to some benchmark algorithms
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie
Automatic trajectory measurement of large numbers of crowded objects
Complex motion patterns of natural systems, such as fish schools, bird
flocks, and cell groups, have attracted great attention from scientists for
years. Trajectory measurement of individuals is vital for quantitative and
high-throughput study of their collective behaviors. However, such data are
rare mainly due to the challenges of detection and tracking of large numbers of
objects with similar visual features and frequent occlusions. We present an
automatic and effective framework to measure trajectories of large numbers of
crowded oval-shaped objects, such as fish and cells. We first use a novel dual
ellipse locator to detect the coarse position of each individual and then
propose a variance minimization active contour method to obtain the optimal
segmentation results. For tracking, cost matrix of assignment between
consecutive frames is trainable via a random forest classifier with many
spatial, texture, and shape features. The optimal trajectories are found for
the whole image sequence by solving two linear assignment problems. We evaluate
the proposed method on many challenging data sets
A unified approach for multi-object triangulation, tracking and camera calibration
Object triangulation, 3-D object tracking, feature correspondence, and camera
calibration are key problems for estimation from camera networks. This paper
addresses these problems within a unified Bayesian framework for joint
multi-object tracking and sensor registration. Given that using standard
filtering approaches for state estimation from cameras is problematic, an
alternative parametrisation is exploited, called disparity space. The disparity
space-based approach for triangulation and object tracking is shown to be more
effective than non-linear versions of the Kalman filter and particle filtering
for non-rectified cameras. The approach for feature correspondence is based on
the Probability Hypothesis Density (PHD) filter, and hence inherits the ability
to update without explicit measurement association, to initiate new targets,
and to discriminate between target and clutter. The PHD filtering approach then
forms the basis of a camera calibration method from static or moving objects.
Results are shown on simulated data
All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
A novel probabilistic perception algorithm is presented as a real-time joint
solution to data association, object tracking, and object classification for an
autonomous ground vehicle in all-weather conditions. The presented algorithm
extends a Rao-Blackwellized Particle Filter originally built with a particle
filter for data association and a Kalman filter for multi-object tracking
(Miller et al. 2011a) to now also include multiple model tracking for
classification. Additionally a state-of-the-art vision detection algorithm that
includes heading information for autonomous ground vehicle (AGV) applications
was implemented. Cornell's AGV from the DARPA Urban Challenge was upgraded and
used to experimentally examine if and how state-of-the-art vision algorithms
can complement or replace lidar and radar sensors. Sensor and algorithm
performance in adverse weather and lighting conditions is tested. Experimental
evaluation demonstrates robust all-weather data association, tracking, and
classification where camera, lidar, and radar sensors complement each other
inside the joint probabilistic perception algorithm.Comment: 35 pages, 21 figures, 14 table
Event-based Camera Pose Tracking using a Generative Event Model
Event-based vision sensors mimic the operation of biological retina and they
represent a major paradigm shift from traditional cameras. Instead of providing
frames of intensity measurements synchronously, at artificially chosen rates,
event-based cameras provide information on brightness changes asynchronously,
when they occur. Such non-redundant pieces of information are called "events".
These sensors overcome some of the limitations of traditional cameras (response
time, bandwidth and dynamic range) but require new methods to deal with the
data they output. We tackle the problem of event-based camera localization in a
known environment, without additional sensing, using a probabilistic generative
event model in a Bayesian filtering framework. Our main contribution is the
design of the likelihood function used in the filter to process the observed
events. Based on the physical characteristics of the sensor and on empirical
evidence of the Gaussian-like distribution of spiked events with respect to the
brightness change, we propose to use the contrast residual as a measure of how
well the estimated pose of the event-based camera and the environment explain
the observed events. The filter allows for localization in the general case of
six degrees-of-freedom motions.Comment: 7 pages, 5 figure
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