Visual Object Tracking on ViCoS Lab

A Long-Term Discriminative Single Shot Segmentation Tracker

Mon, 01 Jan 0001 00:00:00 +0000

State-of-the-art long-term visual object tracking methods are limited to predict target position as an axis-aligned bounding box. Segmentation-based trackers exist, however they do not address long-term disappearances of the target. We propose a long-term discriminative single shot segmentation tracker – D3SLT, which addresses the above shortcomings. The previously developed short-term D3S tracker is upgraded with a global re-detection module, based on an image-wide discriminative correlation filter response and Gaussian motion model. An online learned confidence estimation module is employed for robust estimation target disappearance. Additional backtracking module enables recovery from tracking failures and further improves tracking performance. D3SLT performs close to the state-of-the-art long-term trackers on the bou-nding box based VOT-LT2021 Challenge, achieving F-score of 0.667, while additionally outputting segmentation masks.

Discriminative Correlation Filter with Channel and Spatial Reliability

Mon, 01 Jan 0001 00:00:00 +0000

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard features, HoGs and Colornames, the novel CSR-DCF method – DCF with Channel and Spatial Reliability – achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs in real-time on a CPU.

Fast Spatially Regularized Correlation Filter Tracker

Mon, 01 Jan 0001 00:00:00 +0000

Discriminative correlation filters (DCF) have attracted significant attention of the tracking community. Standard formulation of the DCF affords a closed form solution, but is not robust and constrained to learning and detection using a relatively small search region. Spatial regularization was proposed to address learning from larger regions. But this prohibits a closed form solution and leads to an iterative optimization with significant computational load, resulting in slow model learning and tracking. We propose to reformulate the spatially regularized filter cost function such that it offers an efficient optimization. This significantly speeds up the tracker (approximately 14 times) and results in real-time tracking at the same or better accuracy.