19,919 research outputs found
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
Siam R-CNN: Visual Tracking by Re-Detection
We present Siam R-CNN, a Siamese re-detection architecture which unleashes
the full power of two-stage object detection approaches for visual object
tracking. We combine this with a novel tracklet-based dynamic programming
algorithm, which takes advantage of re-detections of both the first-frame
template and previous-frame predictions, to model the full history of both the
object to be tracked and potential distractor objects. This enables our
approach to make better tracking decisions, as well as to re-detect tracked
objects after long occlusion. Finally, we propose a novel hard example mining
strategy to improve Siam R-CNN's robustness to similar looking objects. Siam
R-CNN achieves the current best performance on ten tracking benchmarks, with
especially strong results for long-term tracking. We make our code and models
available at www.vision.rwth-aachen.de/page/siamrcnn.Comment: CVPR 2020 camera-ready versio
Deformable Object Tracking with Gated Fusion
The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods
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