Plenoptic imaging techniques for improving accuracy and robustness of object tracking

Object tracking is a core technique in many computer vision applications. The problem becomes especially challenging when the target object is fully or even partially occluded. A recent work has shown the feasibility of utilizing plenoptic imaging techniques to resolve such occlusion problems. Specifically, it constructs focal stacks from plenoptic image sequences and selects an optimal image sequences from the stacks that can maximize the tracking accuracy. Even though the technique has proven the merit of using plenoptic images in the object tracking, there is still room for improvement. In this paper, we propose two simple but effective algorithms to improve both accuracy and robustness of object tracking based on plenoptic images. We first propose to use an image sharpening technique to reduce the blur that the refocused images inheritably have. The image sharpening makes the shape of objects more distinct, and thus a higher accuracy in the object tracking can be achieved. We also propose an adaptive bounding box proposal algorithm to overcome difficult cases where the size of the target object in the image space drastically changes. This improves the robustness in the object tracking compared to prior techniques which assumed fixed sized objects. We validate our proposed algorithms on two different scenarios, and the experimental results confirm the benefit of our method.