6,171 research outputs found
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
Semi-supervised Tuning from Temporal Coherence
Recent works demonstrated the usefulness of temporal coherence to regularize
supervised training or to learn invariant features with deep architectures. In
particular, enforcing smooth output changes while presenting temporally-closed
frames from video sequences, proved to be an effective strategy. In this paper
we prove the efficacy of temporal coherence for semi-supervised incremental
tuning. We show that a deep architecture, just mildly trained in a supervised
manner, can progressively improve its classification accuracy, if exposed to
video sequences of unlabeled data. The extent to which, in some cases, a
semi-supervised tuning allows to improve classification accuracy (approaching
the supervised one) is somewhat surprising. A number of control experiments
pointed out the fundamental role of temporal coherence.Comment: Under review as a conference paper at ICLR 201
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
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