7 research outputs found
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Most sparse linear representation-based trackers need to solve a
computationally expensive L1-regularized optimization problem. To address this
problem, we propose a visual tracker based on non-sparse linear
representations, which admit an efficient closed-form solution without
sacrificing accuracy. Moreover, in order to capture the correlation information
between different feature dimensions, we learn a Mahalanobis distance metric in
an online fashion and incorporate the learned metric into the optimization
problem for obtaining the linear representation. We show that online metric
learning using proximity comparison significantly improves the robustness of
the tracking, especially on those sequences exhibiting drastic appearance
changes. Furthermore, in order to prevent the unbounded growth in the number of
training samples for the metric learning, we design a time-weighted reservoir
sampling method to maintain and update limited-sized foreground and background
sample buffers for balancing sample diversity and adaptability. Experimental
results on challenging videos demonstrate the effectiveness and robustness of
the proposed tracker.Comment: Appearing in IEEE Conf. Computer Vision and Pattern Recognition, 201
Tracking and Recognition: A Unified Approach on Tracking and Recognition
This paper proposes a unified approach on tracking and recognition .Object tracking is done at low level and recognition is done at high level. Traditional tracking methods give importance to low level image correspondences between frames. High level image correspondences are used for reliable tracking. Online and Offline models are used for both tracking and recognition which is done simultaneously. Thus high level offline model is combined with low level online model to increase the tracking performance. Onine model used for tracking is given to the video based recognition and at same time offline model plays important role to recognize the category of the object. This method is useful to handle difficult scenarios like abrupt change, background clutter, pose variations, occlusion and morphable objects. This is based on study of different IEEE papers
Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking
An appearance model adaptable to changes in object appearance is critical in visual object tracking. In
this paper, we treat an image patch as a 2-order tensor which preserves the original image structure. We design
two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the
background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of
the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the
transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding
space. In order to encode more discriminant information in the embedding space, we propose a transfer-learningbased
semi-supervised strategy to iteratively adjust the embedding space into which discriminative information
obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph
embedding learning algorithm to visual tracking. The new tracking algorithm captures an object’s appearance
characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results
on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm
Graph mode-based contextual kernels for robust SVM tracking
Visual tracking has been typically solved as a binary classification problem. Most existing trackers only consider the pairwise interactions between samples, and thereby ignore the higher-order contextual interactions, which may lead to the sensitivity to complicated factors such as noises, outliers, background clutters and so on. In this paper, we propose a visual tracker based on support vector machines (SVMs), for which a novel graph mode-based contextual kernel is designed to effectively capture the higher-order contextual information from samples. To do so, we first create a visual graph whose similarity matrix is determined by a baseline visual kernel. Second, a set of high-order contexts are discovered in the visual graph. The problem of discovering these high-order contexts is solved by seeking modes of the visual graph. Each graph mode corresponds to a vertex community termed as a high-order context. Third, we construct a contextual kernel that effectively captures the interaction information between the high-order contexts. Finally, this contextual kernel is embedded into SVMs for robust tracking. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.Xi Li, Anthony Dick, Hanzi Wang, Chunhua Shen, Anton van den Hengelhttp://www.iccv2011.org
An Exploration into Model-Free Online Visual Object Tracking
This thesis presents a thorough investigation of model-free
visual object tracking, a fundamental computer vision task that
is essential for practical video analytics applications. Given
the states of the object in the rst frame, e.g., the position and
size of the target, the computational methods developed and
advanced in this thesis aim at determining target states in
consecutive video frames automatically. In contrast to the
tracking schemes that depend strictly on specic object detectors,
model-free tracking provides conveniently flexible and
competently general solutions where object representations are
initiated in the first frame and adapted in an online manner at
each frame.
We first articulate our motivations and intuitions in Chapter 1,
formulate model-free online visual tracking, illustrate outcomes
on two representative object tracking applications; drone control
and sports video broadcasting analysis, and elaborate other
relevant problems.
In Chapter 2, we review various tracking methodologies employed
by state-ofthe-art trackers and further review related background
knowledge, including several important dataset benchmarks and
workshop challenges, which are widely used for evaluating the
performance of trackers, as well as commonly applied evaluation
protocols in this chapter.
In Chapter 3 through Chapter 6, we then explore the model-free
online visual tracking problem in four different dimensions: 1)
learning a more discriminative classier with a two-layer
classication hierarchy and background contextual clusters; 2)
overcoming the limit of conventionally used local-search scheme
with a global object tracking framework based on instance-specic
object proposals; 3) tracking object affine motion with a
Structured Support Vector Machine (SSVM) framework incorporated
with motion manifold structure; 4) an efficient multiple object
model-free online tracking approach based on a shared pool of
object proposals.
Lastly, as a conclusion and future work outlook, we highlight and
summarize the contribution of this thesis and discuss several
promising research directions in Chapter 7, based on latest work
and their drawbacks of current state-of-the-art trackers