Chaotic target representation for robust object tracking

In this paper, a new object representation method is introduced as an appearance model based on chaos theory. For robust object tracking, the theory is used to extract a deterministic model from irregular patterns of pixel amplitudes in a target region. The object tracking algorithm that accompanies the proposed method involves two steps. First, fractal theory is applied to a compressive sensing method intended to embed an image into a two-dimensional state space during tracking by detection. After an object representation is extracted from an instance, the fractal dimension of the state space is assigned to the importance weight of the instance for efficient online multiple-instance learning. Second, a chaotic map approach is adopted to update the appearance model. Such updating is a crucial step in selecting discriminative and robust features. To highlight the advantages of the algorithm put forward in this work, its accuracy is validated on a large dataset. Results show that the proposed algorithm is more efficient than state-of-the-art tracking algorithms, with the former outperforming the latter under rotation, illumination, and scale changes.