Spatial-Temporal Segmentation-based Tracking

Visual tracking, a fundamental task in computer vision, has been criticized less well-posed since reliable target information only given at first frame. In this case, most of the existing template-matching-based trackers fail to locate the target when non-rigid deformations or variations occur. To address these issues, we propose a principled way to take advantage of the superpixel labeling and discriminative tracking algorithms. For each frame, a correlation tracker is first adopted to provide the coarse target location. Afterwards, a collaborative segmentation approach is advocated to segment the surrounding region of the target into superpixels. Target appearance and motion trajectory are considered as spatial and temporal constrains and incorporated into superpixel labeling module. The fine-segmentation result, in turn, provides a more accurate target status for template updating. The effectiveness of the proposed algorithm is validated through experimental comparison on widely-used tracking benchmark datasets.