Naslov
Real-time Motion Detection in Extremely Subsampled Compressive Sensing Video

Autori
Ralašić, Ivan ; Seršić, Damir

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proc. of the 2019 IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2019), Malaysia, September 17-19, 2019 / - Kuala Lumpur : IEEE SPS, 2019, 198-203

Skup
IEEE International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2019)

Mjesto i datum
Kuala Lumpur, Malezija, 17.09.2019. - 19.09.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
background subtraction, compressive sensing, deep learning, motion detection, reconstruction, video

Sažetak
Compressive sensing (CS) has shown promising results in different areas of signal processing as it provides an elegant framework for simultaneous signal acquisition and compression. Iterative CS reconstruction algorithms limit the practical applicability of CS due to high computational complexity. In this paper, a real- time reconstruction method based on deep neural networks is presented and applied to spatial video CS. In order to show feasibility of learning-based CS approach in real-world applications, we perform motion detection on videos reconstructed from extremely sub-sampled measurements. Experimental results performed on a synthetic dataset show a comparison between performance of motion detection algorithms in the original and the compressively sensed video. The results confirm that most of the information used by standard motion detection algorithms is preserved in the low-dimensional measurement space. Inspired by the obtained results, we propose an adaptive sampling scheme in which CS video camera operates at extremely low measurement rate when there is no motion in the scene. Otherwise, when motion is detected, measurement rate is increased accordingly.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA