TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Koschel, Alan
A1  - Müller, Christoph
A1  - Reiterer, Alexander
T1  - Selection of Key Frames for 3D Reconstruction in Real Time
JF  - Algorithms
N2  - Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruction. Many frames are unsuited for reconstruction as they suffer from motion blur or show too little variation compared to other frames. The camera used within this work has built-in inertial sensors. What if one could use the built-in inertial sensors to select a set of key frames well-suited for 3D reconstruction, free from motion blur and redundancy, in real time? A random forest classifier (RF) is trained by inertial data to determine frames without motion blur and to reduce redundancy. Frames are analyzed by the fast Fourier transformation and Lucas–Kanade method to detect motion blur and moving features in frames to label those correctly to train the RF. We achieve a classifier that omits successfully redundant frames and preserves frames with the required quality but exhibits an unsatisfied performance with respect to ideal frames. A 3D reconstruction by Meshroom shows a better result with selected key frames by the classifier. By extracting frames from video, one can comfortably scan objects and scenes without taking single pictures. Our proposed method automatically extracts the best frames in real time without using complex image-processing algorithms.
KW  - Supervised learning
KW  - Classification
KW  - Key frame selection
KW  - Real time
KW  - Inertial sensing
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-77886
SN  - 1999-4893
SS  - 1999-4893
U6  - https://doi.org/https://doi.org/10.3390/a14110303
DO  - https://doi.org/https://doi.org/10.3390/a14110303
VL  - 14.2021
IS  - 11
SP  - 303
EP  - 319
ER  -