Robust tracking with convolutional neural networks

Boštjan Bohte (2018) Robust tracking with convolutional neural networks. MSc thesis.

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Abstract

Visual object tracking is a process of object(s) localization through the sequence of images. Many successful trackers use convolutional neural networks for tracking. These networks are capable of recognizing object features on an abstract level. We can define trackers as short term and long term trackers with the latter having an additional function which reinitializes their tracking in case of a failure. In our work, we want to improve the tracker that uses convolutional neural network which is already accurate and fast but does not offer the possibility of a long term tracking. We propose a new tracker with a tracking failure detection. This detection predicts with plausibility if the tracker is tracking the object correctly or incorrectly. Based on implemented failure detection, we implement the object detection which finds the tracking object on the image and reinitializes the tracker in case of a predicted tracking failure. With these two features implemented, we fulfill the requirements for the long term tracker. For even more robust long term tracking we propose two methods of updating the template. With the first method, we save templates in an array while with the second method we gradually update the initial template with new examples. We scored the best result in the so far only existing database for long term tracking evaluation. The results are 24% better than those of the currently best published tracker.

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