575 research outputs found
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Object-to-camera motion produces a variety of apparent motion patterns that
significantly affect performance of short-term visual trackers. Despite being
crucial for designing robust trackers, their influence is poorly explored in
standard benchmarks due to weakly defined, biased and overlapping attribute
annotations. In this paper we propose to go beyond pre-recorded benchmarks with
post-hoc annotations by presenting an approach that utilizes omnidirectional
videos to generate realistic, consistently annotated, short-term tracking
scenarios with exactly parameterized motion patterns. We have created an
evaluation system, constructed a fully annotated dataset of omnidirectional
videos and the generators for typical motion patterns. We provide an in-depth
analysis of major tracking paradigms which is complementary to the standard
benchmarks and confirms the expressiveness of our evaluation approach
Good Features to Correlate for Visual Tracking
During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
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