78,399 research outputs found
Reconstructing 3D Motion Trajectory of Large Swarm of Flying Objects
This paper addresses the problem of reconstructing the motion trajectories of
the individuals in a large collection of flying objects using two temporally
synchronized and geometrically calibrated cameras. The 3D trajectory
reconstruction problem involves two challenging tasks - stereo matching and
temporal tracking. Existing methods separate the two and process them one at a
time sequentially, and suffer from frequent irresolvable ambiguities in stereo
matching and in tracking. We unify the two tasks, and propose an optimization
approach to solving stereo matching and temporal tracking simultaneously. It
treats 3D trajectory acquisition problem as selecting appropriate stereo
correspondence out of all possible ones for each object via minimizing a cost
function. Experiment results show that the proposed method offers significant
performance advantage over existing approaches. The proposed method has
successfully been applied to reconstruct 3D motion trajectories of hundreds of
simultaneously flying fruit flies (Drosophila Melanogaster), which could
facilitate the study the insect's collective behavior.Comment: 16 pages,18 figure
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
In order to track the moving objects in long range against occlusion,
interruption, and background clutter, this paper proposes a unified approach
for global trajectory analysis. Instead of the traditional frame-by-frame
tracking, our method recovers target trajectories based on a short sequence of
video frames, e.g. frames. We initially calculate a foreground map at each
frame, as obtained from a state-of-the-art background model. An attribute graph
is then extracted from the foreground map, where the graph vertices are image
primitives represented by the composite features. With this graph
representation, we pose trajectory analysis as a joint task of spatial graph
partitioning and temporal graph matching. The task can be formulated by
maximizing a posteriori under the Bayesian framework, in which we integrate the
spatio-temporal contexts and the appearance models. The probabilistic inference
is achieved by a data-driven Markov Chain Monte Carlo (MCMC) algorithm. Given a
peroid of observed frames, the algorithm simulates a ergodic and aperiodic
Markov Chain, and it visits a sequence of solution states in the joint space of
spatial graph partitioning and temporal graph matching. In the experiments, our
method is tested on several challenging videos from the public datasets of
visual surveillance, and it outperforms the state-of-the-art methods.Comment: 10 pages, 12 figure
SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter
Tracking many cells in time-lapse 3D image sequences is an important
challenging task of bioimage informatics. Motivated by a study of brain-wide 4D
imaging of neural activity in C. elegans, we present a new method of multi-cell
tracking. Data types to which the method is applicable are characterized as
follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to
distinguish cells based only on shape and size, (iii) the number of imaged
cells ranges in several hundreds, (iv) moves of nearly-located cells are
strongly correlated and (v) cells do not divide. We developed a tracking
software suite which we call SPF-CellTracker. Incorporating dependency on
cells' moves into prediction model is the key to reduce the tracking errors:
cell-switching and coalescence of tracked positions. We model target cells'
correlated moves as a Markov random field and we also derive a fast computation
algorithm, which we call spatial particle filter. With the live-imaging data of
nuclei of C. elegans neurons in which approximately 120 nuclei of neurons are
imaged, we demonstrate an advantage of the proposed method over the standard
particle filter and a method developed by Tokunaga et al. (2014).Comment: 14 pages, 6 figure
Scaling Data Association for Hypothesis-Oriented MHT
Multi-hypothesis tracking is a flexible and intuitive approach to tracking
multiple nearby objects. However, the original formulation of its data
association step is widely thought to scale poorly with the number of tracked
objects. We propose enhancements including handling undetected objects and
false measurements without inflating the size of the problem, early stopping
during solution calculation, and providing for sparse or gated input. These
changes collectively improve the computational time and space requirements of
data association so that hundreds or thousands of hypotheses over hundreds of
objects may be considered in real time. A multi-sensor simulation demonstrates
that scaling up the hypothesis count can significantly improve performance in
some applications.Comment: To appear in IEEE FUSION 201
Robust Visual Tracking via Hierarchical Convolutional Features
In this paper, we propose to exploit the rich hierarchical features of deep
convolutional neural networks to improve the accuracy and robustness of visual
tracking. Deep neural networks trained on object recognition datasets consist
of multiple convolutional layers. These layers encode target appearance with
different levels of abstraction. For example, the outputs of the last
convolutional layers encode the semantic information of targets and such
representations are invariant to significant appearance variations. However,
their spatial resolutions are too coarse to precisely localize the target. In
contrast, features from earlier convolutional layers provide more precise
localization but are less invariant to appearance changes. We interpret the
hierarchical features of convolutional layers as a nonlinear counterpart of an
image pyramid representation and explicitly exploit these multiple levels of
abstraction to represent target objects. Specifically, we learn adaptive
correlation filters on the outputs from each convolutional layer to encode the
target appearance. We infer the maximum response of each layer to locate
targets in a coarse-to-fine manner. To further handle the issues with scale
estimation and re-detecting target objects from tracking failures caused by
heavy occlusion or out-of-the-view movement, we conservatively learn another
correlation filter, that maintains a long-term memory of target appearance, as
a discriminative classifier. We apply the classifier to two types of object
proposals: (1) proposals with a small step size and tightly around the
estimated location for scale estimation; and (2) proposals with large step size
and across the whole image for target re-detection. Extensive experimental
results on large-scale benchmark datasets show that the proposed algorithm
performs favorably against state-of-the-art tracking methods.Comment: To appear in T-PAMI 2018, project page at
https://sites.google.com/site/chaoma99/hcft-trackin
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
This paper proposes a technique for the unsupervised detection and tracking
of arbitrary objects in videos. It is intended to reduce the need for detection
and localization methods tailored to specific object types and serve as a
general framework applicable to videos with varied objects, backgrounds, and
image qualities. The technique uses a dependent Dirichlet process mixture
(DDPM) known as the Generalized Polya Urn (GPUDDPM) to model image pixel data
that can be easily and efficiently extracted from the regions in a video that
represent objects. This paper describes a specific implementation of the model
using spatial and color pixel data extracted via frame differencing and gives
two algorithms for performing inference in the model to accomplish detection
and tracking. This technique is demonstrated on multiple synthetic and
benchmark video datasets that illustrate its ability to, without modification,
detect and track objects with diverse physical characteristics moving over
non-uniform backgrounds and through occlusion.Comment: 21 pages, 7 figure
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
To accomplish tasks in human-centric indoor environments, robots need to
represent and understand the world in terms of objects and their attributes. We
refer to this attribute-based representation as a world model, and consider how
to acquire it via noisy perception and maintain it over time, as objects are
added, changed, and removed in the world. Previous work has framed this as
multiple-target tracking problem, where objects are potentially in motion at
all times. Although this approach is general, it is computationally expensive.
We argue that such generality is not needed in typical world modeling tasks,
where objects only change state occasionally. More efficient approaches are
enabled by restricting ourselves to such semi-static environments.
We consider a previously-proposed clustering-based world modeling approach
that assumed static environments, and extend it to semi-static domains by
applying a dependent Dirichlet-process (DDP) mixture model. We derive a novel
MAP inference algorithm under this model, subject to data association
constraints. We demonstrate our approach improves computational performance in
semi-static environments
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Self-driving vehicle vision systems must deal with an extremely broad and
challenging set of scenes. They can potentially exploit an enormous amount of
training data collected from vehicles in the field, but the volumes are too
large to train offline naively. Not all training instances are equally valuable
though, and importance sampling can be used to prioritize which training images
to collect. This approach assumes that objects in images are labeled with high
accuracy. To generate accurate labels in the field, we exploit the
spatio-temporal coherence of vehicle video. We use a near-to-far labeling
strategy by first labeling large, close objects in the video, and tracking them
back in time to induce labels on small distant presentations of those objects.
In this paper we demonstrate the feasibility of this approach in several steps.
First, we note that an optimal subset (relative to all the objects encountered
and labeled) of labeled objects in images can be obtained by importance
sampling using gradients of the recognition network. Next we show that these
gradients can be approximated with very low error using the loss function,
which is already available when the CNN is running inference. Then, we
generalize these results to objects in a larger scene using an object detection
system. Finally, we describe a self-labeling scheme using object tracking.
Objects are tracked back in time (near-to-far) and labels of near objects are
used to check accuracy of those objects in the far field. We then evaluate the
accuracy of models trained on importance sampled data vs models trained on
complete data
Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos
Optimized scene representation is an important characteristic of a framework
for detecting abnormalities on live videos. One of the challenges for detecting
abnormalities in live videos is real-time detection of objects in a
non-parametric way. Another challenge is to efficiently represent the state of
objects temporally across frames. In this paper, a Gibbs sampling based
heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC)
has been proposed to cluster pixels with motion. Pixel motion is first detected
using optical flow and a Bayesian algorithm has been applied to associate
pixels belonging to similar cluster in subsequent frames. The algorithm is fast
and produces accurate results in time, where is the number of
clusters and the number of pixels. Our experimental validation with
publicly available datasets reveals that the proposed framework has good
potential to open-up new opportunities for real-time traffic analysis
Tracking rapid intracellular movements: A Bayesian random set approach
We focus on the biological problem of tracking organelles as they move
through cells. In the past, most intracellular movements were recorded
manually, however, the results are too incomplete to capture the full
complexity of organelle motions. An automated tracking algorithm promises to
provide a complete analysis of noisy microscopy data. In this paper, we adopt
statistical techniques from a Bayesian random set point of view. Instead of
considering each individual organelle, we examine a random set whose members
are the organelle states and we establish a Bayesian filtering algorithm
involving such set states. The propagated multi-object densities are
approximated using a Gaussian mixture scheme. Our algorithm is applied to
synthetic and experimental data.Comment: Published at http://dx.doi.org/10.1214/15-AOAS819 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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