An improved visual model for tracking by segmentation
Year: 2020
Type: dissertation
Abstract: As if in response to the increased focus of the field on visual object tracking and video object segmentation, this work features several trackers escalating the associations between the two disciplines. These trackers, in particular, build upon an existing D3S tracker that has the capacity to produce both highly-reliable localization as well as an accurate segmentation of the target. Furthermore, said products are used in future target state inference to inform the process and achieve excellent tracking performance. In recognition of the benefits reaped by involving segmentation in visual object tracking, this work proposes several trackers in an effort to further both the accuracy and robustness of the D3S, as well as to improve its speed of inference. Novel trackers are compounded from existing components of the D3S implementation along with other constituents giving prominence to the latest advancements in the field. Namely, the two backbones of the original implementation are merged into a single backbone, CARAFE modules are instated to replace the bilinear upsampling stages, Octave convolution is introduced to improve the speed of feature extraction and the attention mechanism is implemented to incorporate contextual information into the tracking process. Alongside this, the lack of dataset diversity inspires a synthetic dataset to be constructed and used in pre-training stages of representation learning. Finally, the suitability of proposed tracking architectures is determined through rigorous evaluation.
Author: Tilen Nedanovski
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3100855086