1. Compressive Video Sensing & Classification
2. GPU accelerated CVS
3. Compressed Gated Range Sensing (CGRS)
4. Compressed Sensing & Sparsity in Imaging
5. Compressed Sensing in Localization
6. Fundamentals of CS Acquisition and Reconstruction

Compressed Video Sensing

Lightweight remote imaging systems have been increasingly used in surveillance and reconnaissance. Nevertheless, the limited power, processing and bandwidth resources is a major issue for the existing solutions, not well addressed by the standard video compression techniques. On the one hand, the MPEGx family achieves a balance between the reconstruction quality and the required bit-rate by exploiting potential intra- and interframe redundancies at the encoder, but at the cost of increased memory and processing demands. On the other hand, the M-JPEG approach consists of a computationally efficient encoding process, with the drawback of resulting in much higher bit-rates.

The proposed CS based architecture copes with the growing compression ratios, required for all remote imaging applications, by exploiting the inherent property of compressive sensing (CS), acting simultaneously as a sensing and compression framework. The proposed compressive video sensing (CVS) system incorporates the advantages of a very simple CS-based encoding process, while putting the main computational burden at the decoder combining the efficiency of a motion compensation procedure for the extraction of inter-frame correlations, along with an additional super-resolution step to enhance the quality of reconstructed frames. The experimental results reveal a significant improvement of the reconstruction quality when compared with M-JPEG, at equal or even lower bit-rates.

Compressed Video Classification

We introduce an architecture for addressing the problem of video classification based on a set of compressed features, without the need of accessing the original full-resolution video data. In particular, the video frames are acquired directly in a compressed domain by means of random projections associated with a set of compressive measurements. This initial dimensionality reduction step is followed by distance metric learning for the construction of an informative distance matrix, which is then embedded in a manifold learning approach to increase the discriminative power of the random measurements in a lower-dimensional space. Classification results using a set of activity videos suggest that the proposed approach can be used effectively in cases when the acquisition and processing of full-resolution video data is characterized by increased consumption of the available power, memory and bandwidth, which may impede the operation of systems with limited resources.