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Integral imaging has proven useful for three-dimensional (3D) object visualization in adverse environmental conditions such as partial occlusion and low light. This paper considers the problem of 3D object tracking. Two-dimensional (2D) object tracking within a scene is an active research area. Several recent algorithms use object detection methods to obtain 2D bounding boxes around objects of interest in each frame. Then, one bounding box can be selected out of many for each object of interest using motion prediction algorithms. Many of these algorithms rely on images obtained using traditional 2D imaging systems. A growing literature demonstrates the advantage of using 3D integral imaging instead of traditional 2D imaging for object detection and visualization in adverse environmental conditions. Integral imaging’s depth sectioning ability has also proven beneficial for object detection and visualization. Integral imaging captures an object’s depth in addition to its 2D spatial position in each frame. A recent study uses integral imaging for the 3D reconstruction of the scene for object classification and utilizes the mutual information between the object’s bounding box in this 3D reconstructed scene and the 2D central perspective to achieve passive depth estimation. We build over this method by using Bayesian optimization to track the object’s depth in as few 3D reconstructions as possible. We study the performance of our approach on laboratory scenes with occluded objects moving in 3D and show that the proposed approach outperforms 2D object tracking. In our experimental setup, mutual information-based depth estimation with Bayesian optimization achieves depth tracking with as few as two 3D reconstructions per frame which corresponds to the theoretical minimum number of 3D reconstructions required for depth estimation. To the best of our knowledge, this is the first report on 3D object tracking using the proposed approach.
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Method for minimizing lens breathing with one moving group
Lens breathing in movie cameras is the change in the overall content of a scene while bringing subjects located at different depths into focus. This paper presents a method for minimizing lens breathing or changing angular field-of-view while maintaining perspective by moving only one lens group. To maintain perspective, the stop is placed in a fixed position where no elements between the scene and the stop can move, thus fixing the entrance pupil in one location relative to the object fields. The result is perspective invariance while refocusing the lens. Using paraxial optics, we solve for the moving group's position to focus on every object position and eliminate breathing between the minimum and maximum object distances. We investigate the solution space for optical systems with two positive groups or a positive and a negative group (i.e., retrofocus and telephoto systems). We explain how to apply this paraxial solution to existing systems to minimize breathing. The results for two systems altered using this method are presented. Breathing improved by two orders of magnitude in both cases, and performance specifications were still met when compared to the initial systems.
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- PAR ID:
- 10531214
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 30
- Issue:
- 11
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 19494
- Size(s):
- Article No. 19494
- Sponsoring Org:
- National Science Foundation
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