2 research outputs found
Deep Motion Features for Visual Tracking
Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite their success, these features only capture appearance
information. On the other hand, motion cues provide discriminative and
complementary information that can improve tracking performance. Contrary to
visual tracking, deep motion features have been successfully applied for action
recognition and video classification tasks. Typically, the motion features are
learned by training a CNN on optical flow images extracted from large amounts
of labeled videos.
This paper presents an investigation of the impact of deep motion features in
a tracking-by-detection framework. We further show that hand-crafted, deep RGB,
and deep motion features contain complementary information. To the best of our
knowledge, we are the first to propose fusing appearance information with deep
motion features for visual tracking. Comprehensive experiments clearly suggest
that our fusion approach with deep motion features outperforms standard methods
relying on appearance information alone.Comment: ICPR 2016. Best paper award in the "Computer Vision and Robot Vision"
trac
Visuell följning med hjälp av djup inlärning och optiskt flöde
Generic visual tracking is a challenging computer vision problem, where the position of a specified target is estimated through a sequence of frames. The only given information is the initial location of the target. Therefore, the tracker has to adapt and learn any kind of object, which it describes through visual features used to differentiate target from background. Standard appearance features only capture momentary visual information. This master’s thesis investigates the use of deep features extracted through optical flow images processed in a deep convolutional network. The optical flow is calculated using two consecutive images, and thereby captures the dynamic nature of the scene. Results show that this information is complementary to the standard appearance features, and improves performance of the tracker. Deep features are typically very high dimensional. Employing dimensionality reduction can increase both the efficiency and performance of the tracker. As a second aim in this thesis, PCA and PLS were evaluated and compared. The evaluations show that the two methods are almost equal in performance, with PLS actually receiving slightly better score than the popular PCA. The final proposed tracker was evaluated on three challenging datasets, and was shown to outperform other state-of-the-art trackers