Realtime Motion Detection in Extremely Subsampled Compressive Sensing Video 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 realtime reconstruction method based on deep neural networks is presented and applied to spatial video CS. In order to show feasibility of learningbased CS approach in realworld applications, we perform motion detection on videos reconstructed from extremely subsampled 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 lowdimensional 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.

Realtime Compressive Video Reconstruction for Spatial Multiplexing Cameras In this work, we tackle the problem of realtime compressive video reconstruction in spatial multiplexing cameras using spatially modulated and undersampled focal plane array data. In this setting, a spatial light modulator (SLM) modulates the scene in the image domain by blocking some of the pixels at a higher resolution level, using an SLM such as a digital micromirror device. Here, we first propose a practical warmstarting method to achieve realtime reconstruction of streaming video. We then extend it by adapting a multihypothesis technique that takes temporal consistency into account using residual reconstruction between frames. We implement the algorithms on a graphics processing unit and give an extensive analysis of the number of required iterations for different imaging settings. We conclude that the proposed methods achieve video reconstruction in realtime from highly undersampled compressed measurements and provide high quality frames in terms of PSNR and SSIM metrics.