IPST: Incremental Pictorial Structures for Model-Free Tracking of Deformable Objects
Model-free tracking is a well-studied task in computer vision. Typically, a rectangular bounding box containing a single object is provided in the first (few) frame(s) and then the method tracks the object in the rest frames. However, for deformable objects (e.g. faces, bodies) the single bounding box scenario is suboptimal; a part-based approach would be more effective. The current state-of-the-art part-based approach is incrementally trained discriminative deformable part models (DPM). Nevertheless, training discriminative DPMs with one or a few examples poses a huge challenge. We argue that a generative model is a better fit for the task. To that end, we utilize the powerful pictorial structures, which we augment with incremental updates to account for object adaptations. Our proposed incremental pictorial structures, which we call incremental pictorial structures for model-free tracking (IPST), are experimentally validated in different scenarios. In a thorough experimentation we demonstrate that IPST outperforms the existing model-free methods in facial landmark tracking, body tracking, and animal tracking (newly introduced to verify the strength in ad hoc cases).