13,633 research outputs found
Real-time tracker with fast recovery from target loss
In this paper, we introduce a variation of a state-of-the-art real-time
tracker (CFNet), which adds to the original algorithm robustness to target loss
without a significant computational overhead. The new method is based on the
assumption that the feature map can be used to estimate the tracking confidence
more accurately. When the confidence is low, we avoid updating the object's
position through the feature map; instead, the tracker passes to a single-frame
failure mode, during which the patch's low-level visual content is used to
swiftly update the object's position, before recovering from the target loss in
the next frame. The experimental evidence provided by evaluating the method on
several tracking datasets validates both the theoretical assumption that the
feature map is associated to tracking confidence, and that the proposed
implementation can achieve target recovery in multiple scenarios, without
compromising the real-time performance.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0784
Siamese Instance Search for Tracking
In this paper we present a tracker, which is radically different from
state-of-the-art trackers: we apply no model updating, no occlusion detection,
no combination of trackers, no geometric matching, and still deliver
state-of-the-art tracking performance, as demonstrated on the popular online
tracking benchmark (OTB) and six very challenging YouTube videos. The presented
tracker simply matches the initial patch of the target in the first frame with
candidates in a new frame and returns the most similar patch by a learned
matching function. The strength of the matching function comes from being
extensively trained generically, i.e., without any data of the target, using a
Siamese deep neural network, which we design for tracking. Once learned, the
matching function is used as is, without any adapting, to track previously
unseen targets. It turns out that the learned matching function is so powerful
that a simple tracker built upon it, coined Siamese INstance search Tracker,
SINT, which only uses the original observation of the target from the first
frame, suffices to reach state-of-the-art performance. Further, we show the
proposed tracker even allows for target re-identification after the target was
absent for a complete video shot.Comment: This paper is accepted to the IEEE Conference on Computer Vision and
Pattern Recognition, 201
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