Visual object tracking using semi supervised convolutional filters

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2020-10-15
Sevindik, Emir Can
Visual object tracking aims to find a single object position in a video frame, when a annotated bounding box is provided in the first frame. Correlation filters have always produced excellent results in terms of accuracy, while enjoying quite low computational complexity. The main property of correlation filter based trackers is to find a filter that can generate high values around the true target object location, whereas relatively low values for the locations away from the object. Recently, deep learning based methods have emerged to learn the optimal discriminative features to be utilized in correlation filters with promising results. Training of such deep feature extractors are usually performed by using both supervised and unsupervised learning techniques. In this thesis, the impact of semi supervised convolutional filters for the visual tracking problem is investigated in order to obtain robust features predicting the object location with high accuracy and being invariant to any kind of appearance change. Two different semi-supervision techniques are proposed and trained on ILSVRC2015 and TrackingNet dataset separately. They are also tested on widely used OTB50 and OTB100 tracking benchmark datasets. Semi-supervision v on ILSVRC2015 dataset leads 1.1% gain on success plot AUC value, 2.4% increase on precision plot AUC value and 2.2% increase on success rate in terms of OTB50 benchmark parameters. Similarly on OTB100 test set, 1.9% gain on success plot AUC value, 2.0% on precision plot AUC value and 2.8% increase on success rate is observed during semi-supervised experiments. In addition to the semi supervision methods, two joint supervision methodologies are also examined to observe the performance differences. The results show that both semi-supervision and joint supervision perform better than the fully supervised models, and such techniques still have superiorities on each other for different occasions.

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Citation Formats
E. C. Sevindik, “Visual object tracking using semi supervised convolutional filters,” M.S. - Master of Science, Middle East Technical University, 2020.