64,357 research outputs found
Online Tracking Parameter Adaptation based on Evaluation
Parameter tuning is a common issue for many tracking algorithms. In order to
solve this problem, this paper proposes an online parameter tuning to adapt a
tracking algorithm to various scene contexts. In an offline training phase,
this approach learns how to tune the tracker parameters to cope with different
contexts. In the online control phase, once the tracking quality is evaluated
as not good enough, the proposed approach computes the current context and
tunes the tracking parameters using the learned values. The experimental
results show that the proposed approach improves the performance of the
tracking algorithm and outperforms recent state of the art trackers. This paper
brings two contributions: (1) an online tracking evaluation, and (2) a method
to adapt online tracking parameters to scene contexts.Comment: IEEE International Conference on Advanced Video and Signal-based
Surveillance (2013
Automatic Parameter Adaptation for Multi-object Tracking
Object tracking quality usually depends on video context (e.g. object
occlusion level, object density). In order to decrease this dependency, this
paper presents a learning approach to adapt the tracker parameters to the
context variations. In an offline phase, satisfactory tracking parameters are
learned for video context clusters. In the online control phase, once a context
change is detected, the tracking parameters are tuned using the learned values.
The experimental results show that the proposed approach outperforms the recent
trackers in state of the art. This paper brings two contributions: (1) a
classification method of video sequences to learn offline tracking parameters,
(2) a new method to tune online tracking parameters using tracking context.Comment: International Conference on Computer Vision Systems (ICVS) (2013
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
We tackle the task of semi-supervised video object segmentation, i.e.
segmenting the pixels belonging to an object in the video using the ground
truth pixel mask for the first frame. We build on the recently introduced
one-shot video object segmentation (OSVOS) approach which uses a pretrained
network and fine-tunes it on the first frame. While achieving impressive
performance, at test time OSVOS uses the fine-tuned network in unchanged form
and is not able to adapt to large changes in object appearance. To overcome
this limitation, we propose Online Adaptive Video Object Segmentation (OnAVOS)
which updates the network online using training examples selected based on the
confidence of the network and the spatial configuration. Additionally, we add a
pretraining step based on objectness, which is learned on PASCAL. Our
experiments show that both extensions are highly effective and improve the
state of the art on DAVIS to an intersection-over-union score of 85.7%.Comment: Accepted at BMVC 2017. This version contains minor changes for the
camera ready versio
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Learning feed-forward one-shot learners
One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn the parameters of a deep model in one shot. We construct the
learner as a second deep network, called a learnet, which predicts the
parameters of a pupil network from a single exemplar. In this manner we obtain
an efficient feed-forward one-shot learner, trained end-to-end by minimizing a
one-shot classification objective in a learning to learn formulation. In order
to make the construction feasible, we propose a number of factorizations of the
parameters of the pupil network. We demonstrate encouraging results by learning
characters from single exemplars in Omniglot, and by tracking visual objects
from a single initial exemplar in the Visual Object Tracking benchmark.Comment: The first three authors contributed equally, and are listed in
alphabetical orde
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision. Recently, some
deep-learning-based tracking algorithms have been achieving record-breaking
performances. However, due to the high complexity of deep learning, most deep
trackers suffer from low tracking speed, and thus are impractical in many
real-world applications. Some new deep trackers with smaller network structure
achieve high efficiency while at the cost of significant decrease on precision.
In this paper, we propose to transfer the feature for image classification to
the visual tracking domain via convolutional channel reductions. The channel
reduction could be simply viewed as an additional convolutional layer with the
specific task. It not only extracts useful information for object tracking but
also significantly increases the tracking speed. To better accommodate the
useful feature of the target in different scales, the adaptation filters are
designed with different sizes. The yielded visual tracker is real-time and also
illustrates the state-of-the-art accuracies in the experiment involving two
well-adopted benchmarks with more than 100 test videos.Comment: 6 page
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