Efficient depth localization of objects in a 3D space using computational integral imaging

Michael Kadosh; Anton Fraiman; Eli Peli; Yitzhak Yitzhaky

doi:10.1117/12.2683627

17 October 2023 Efficient depth localization of objects in a 3D space using computational integral imaging

Michael Kadosh, Anton Fraiman, Eli Peli, Yitzhak Yitzhaky

Author Affiliations +

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 1274208 (2023) https://doi.org/10.1117/12.2683627
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

Accurate localization and recognition of objects in the three dimensional (3D) space can be useful in security and defence applications such as scene monitoring and surveillance. A main challenge in 3D object localization is to find the depth location of objects. We demonstrate here the use of a camera array with computational integral imaging to estimate depth locations of objects detected and classified in a two-dimensional (2D) image. Following an initial 2D object detection in the scene using a pre-trained deep learning model, a computational integral imaging is employed within the detected objects’ bounding boxes, and by a straightforward blur measure analysis, we estimate the objects’ depth locations.

Conference Presentation

Citation Download Citation

Michael Kadosh, Anton Fraiman, Eli Peli, and Yitzhak Yitzhaky "Efficient depth localization of objects in a 3D space using computational integral imaging", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 1274208 (17 October 2023); https://doi.org/10.1117/12.2683627

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