Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking
This work addresses the challenge of robust object tracking in dynamic environments for computer vision applications, representing an incremental improvement through a novel fusion method.
The authors tackled the problem of robust visual tracking by proposing a unified graph fusion framework that integrates sparse and dense features to adapt to appearance variations, resulting in improved resilience to object deformations, fast motion, and occlusion.
Visual Tracking is a complex problem due to unconstrained appearance variations and dynamic environment. Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work. To this end, we propose a robust object tracking framework based on Unified Graph Fusion (UGF) of multi-cue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multi-cue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric.