Effective Object Tracking in Unstructured Crowd Scenes
This addresses the problem of robust object tracking in complex, unstructured crowd environments for applications like surveillance, but appears incremental as it builds on existing descriptors and mean shift frameworks.
The paper tackles object tracking in unstructured crowd scenes by proposing a rotation variant Oriented Texture Curve descriptor combined with a mean shift algorithm, achieving results compared to state-of-the-art methods on challenging videos.
In this paper, we are presenting a rotation variant Oriented Texture Curve (OTC) descriptor based mean shift algorithm for tracking an object in an unstructured crowd scene. The proposed algorithm works by first obtaining the OTC features for a manually selected object target, then a visual vocabulary is created by using all the OTC features of the target. The target histogram is obtained using codebook encoding method which is then used in mean shift framework to perform similarity search. Results are obtained on different videos of challenging scenes and the comparison of the proposed approach with several state-of-the-art approaches are provided. The analysis shows the advantages and limitations of the proposed approach for tracking an object in unstructured crowd scenes.