6,602 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
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing
In this paper, we present a new method for detecting road users in an urban
environment which leads to an improvement in multiple object tracking. Our
method takes as an input a foreground image and improves the object detection
and segmentation. This new image can be used as an input to trackers that use
foreground blobs from background subtraction. The first step is to create
foreground images for all the frames in an urban video. Then, starting from the
original blobs of the foreground image, we merge the blobs that are close to
one another and that have similar optical flow. The next step is extracting the
edges of the different objects to detect multiple objects that might be very
close (and be merged in the same blob) and to adjust the size of the original
blobs. At the same time, we use the optical flow to detect occlusion of objects
that are moving in opposite directions. Finally, we make a decision on which
information we keep in order to construct a new foreground image with blobs
that can be used for tracking. The system is validated on four videos of an
urban traffic dataset. Our method improves the recall and precision metrics for
the object detection task compared to the vanilla background subtraction method
and improves the CLEAR MOT metrics in the tracking tasks for most videos
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
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
On fast multiplication of a matrix by its transpose
We present a non-commutative algorithm for the multiplication of a
2x2-block-matrix by its transpose using 5 block products (3 recursive calls and
2 general products) over C or any finite field.We use geometric considerations
on the space of bilinear forms describing 2x2 matrix products to obtain this
algorithm and we show how to reduce the number of involved additions.The
resulting algorithm for arbitrary dimensions is a reduction of multiplication
of a matrix by its transpose to general matrix product, improving by a constant
factor previously known reductions.Finally we propose schedules with low memory
footprint that support a fast and memory efficient practical implementation
over a finite field.To conclude, we show how to use our result in LDLT
factorization.Comment: ISSAC 2020, Jul 2020, Kalamata, Greec
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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