Towards real-time change detection in videos based on existing 3D models

Boitumelo Ruf; Tobias Schuchert

doi:10.1117/12.2241992

18 October 2016 Towards real-time change detection in videos based on existing 3D models

Boitumelo Ruf, Tobias Schuchert

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 100041H (2016) https://doi.org/10.1117/12.2241992
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Image based change detection is of great importance for security applications, such as surveillance and reconnaissance, in order to find new, modified or removed objects. Such change detection can generally be performed by co-registration and comparison of two or more images. However, existing 3d objects, such as buildings, may lead to parallax artifacts in case of inaccurate or missing 3d information, which may distort the results in the image comparison process, especially when the images are acquired from aerial platforms like small unmanned aerial vehicles (UAVs). Furthermore, considering only intensity information may lead to failures in detection of changes in the 3d structure of objects. To overcome this problem, we present an approach that uses Structure-from-Motion (SfM) to compute depth information, with which a 3d change detection can be performed against an existing 3d model. Our approach is capable of the change detection in real-time. We use the input frames with the corresponding camera poses to compute dense depth maps by an image-based depth estimation algorithm. Additionally we synthesize a second set of depth maps, by rendering the existing 3d model from the same camera poses as those of the image-based depth map. The actual change detection is performed by comparing the two sets of depth maps with each other. Our method is evaluated on synthetic test data with corresponding ground truth as well as on real image test data.

Citation Download Citation

Boitumelo Ruf and Tobias Schuchert "Towards real-time change detection in videos based on existing 3D models", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041H (18 October 2016); https://doi.org/10.1117/12.2241992

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