4,540 research outputs found
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. Compared to tracking with a still camera, the
images captured with a PTZ camera are highly dynamic in nature because the
camera can perform large motion resulting in quickly changing capture
conditions. Furthermore, tracking with a PTZ camera involves camera control to
position the camera on the target. For successful tracking and camera control,
the tracker must be fast enough, or has to be able to predict accurately the
next position of the target. Therefore, standard benchmarks do not allow to
assess properly the quality of a tracker for the PTZ scenario. In this work, we
use a virtual PTZ framework to evaluate different tracking algorithms and
compare their performances. We also extend the framework to add target position
prediction for the next frame, accounting for camera motion and processing
delays. By doing this, we can assess if predicting can make long-term tracking
more robust as it may help slower algorithms for keeping the target in the
field of view of the camera. Results confirm that both speed and robustness are
required for tracking under the PTZ scenario.Comment: 6 pages, 2 figures, International Conference on Pattern Recognition
and Artificial Intelligence 201
Automatic Image Registration in Infrared-Visible Videos using Polygon Vertices
In this paper, an automatic method is proposed to perform image registration
in visible and infrared pair of video sequences for multiple targets. In
multimodal image analysis like image fusion systems, color and IR sensors are
placed close to each other and capture a same scene simultaneously, but the
videos are not properly aligned by default because of different fields of view,
image capturing information, working principle and other camera specifications.
Because the scenes are usually not planar, alignment needs to be performed
continuously by extracting relevant common information. In this paper, we
approximate the shape of the targets by polygons and use affine transformation
for aligning the two video sequences. After background subtraction, keypoints
on the contour of the foreground blobs are detected using DCE (Discrete Curve
Evolution)technique. These keypoints are then described by the local shape at
each point of the obtained polygon. The keypoints are matched based on the
convexity of polygon's vertices and Euclidean distance between them. Only good
matches for each local shape polygon in a frame, are kept. To achieve a global
affine transformation that maximises the overlapping of infrared and visible
foreground pixels, the matched keypoints of each local shape polygon are stored
temporally in a buffer for a few number of frames. The matrix is evaluated at
each frame using the temporal buffer and the best matrix is selected, based on
an overlapping ratio criterion. Our experimental results demonstrate that this
method can provide highly accurate registered images and that we outperform a
previous related method
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Conventional methods of estimating latent behaviour generally use attitudinal
questions which are subjective and these survey questions may not always be
available. We hypothesize that an alternative approach can be used for latent
variable estimation through an undirected graphical models. For instance,
non-parametric artificial neural networks. In this study, we explore the use of
generative non-parametric modelling methods to estimate latent variables from
prior choice distribution without the conventional use of measurement
indicators. A restricted Boltzmann machine is used to represent latent
behaviour factors by analyzing the relationship information between the
observed choices and explanatory variables. The algorithm is adapted for latent
behaviour analysis in discrete choice scenario and we use a graphical approach
to evaluate and understand the semantic meaning from estimated parameter vector
values. We illustrate our methodology on a financial instrument choice dataset
and perform statistical analysis on parameter sensitivity and stability. Our
findings show that through non-parametric statistical tests, we can extract
useful latent information on the behaviour of latent constructs through machine
learning methods and present strong and significant influence on the choice
process. Furthermore, our modelling framework shows robustness in input
variability through sampling and validation
Background subtraction based on Local Shape
We present a novel approach to background subtraction that is based on the
local shape of small image regions. In our approach, an image region centered
on a pixel is mod-eled using the local self-similarity descriptor. We aim at
obtaining a reliable change detection based on local shape change in an image
when foreground objects are moving. The method first builds a background model
and compares the local self-similarities between the background model and the
subsequent frames to distinguish background and foreground objects.
Post-processing is then used to refine the boundaries of moving objects.
Results show that this approach is promising as the foregrounds obtained are
com-plete, although they often include shadows.Comment: 4 pages, 5 figures, 3 tabl
Volunteering a Public Service: An Experimental Investigation
In some public goods environments it may be advantageous for heterogeneous groups to be coordinated by a single individual. This “volunteer” will bear private costs for acting as the leader while enabling each member of the group to achieve maximum potential gains. This environment is modeled as a War of Attrition game in which everyone can wait for someone else to volunteer. Since these games generally have multiple Nash equilibria but a unique subgameperfect equilibrium, we tested experimentally the predictive power of the subgame-perfection criterion. Our data contradict that subjects saw the subgame-perfect strategy combination as the obvious way to play the game. An alternative behavioral hypothesis – that subjects were unable to predict accurately how their opponents would play and tried to maximize their expected payoff – is proposed. This hypothesis fits the observed data generally well.
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection
In this paper, we propose to combine detections from background subtraction
and from a multiclass object detector for multiple object tracking (MOT) in
urban traffic scenes. These objects are associated across frames using spatial,
colour and class label information, and trajectory prediction is evaluated to
yield the final MOT outputs. The proposed method was tested on the Urban
tracker dataset and shows competitive performances compared to state-of-the-art
approaches. Results show that the integration of different detection inputs
remains a challenging task that greatly affects the MOT performance
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