We model the spatio-temporal variations of the shape of objects in a video sequence using a unique SVD-like decomposition. The decomposition is used to compute shape features, which form an approximation of the original shape sequence. The features are used to train separate classifiers using multi-class boosting strategy. We demonstrate the effectiveness of the proposed approach for shape recognition using the China Lake outdoor surveillance dataset; and compare the results using mean shapes as baseline. We illustrate the usefulness of the proposed shape features for detecting shapes of interest using the SIG group activity dataset.

Conventional software radios take advantage of vastly improved analog to digital converters (ADCs) and digital signal processing (DSP) hardware. Our approach, which we refer to as virtual radios, also depends upon high performance ADCs. However, rather than use DSPs, we have chosen to ride the curve of rapidly improving workstation hardware. We use wideband digitization and then perform all of the digital signal processing in user space on a general purpose workstation. This approach allows us to experiment with new approaches to signal processing that exploit the hardware and software resources of the workstation. Furthermore, it allows us to experiment with different ways of structuring systems in which the radio component of communication devices is integrated with higher-level applications. This paper describes the design and performance of an environment we have constructed that facilitates building virtual radios and of two applications built using that environment. The environment consists of an input/output (I/O) subsystem that provides high bandwidth low latency user-level access to digitized signals and a programming environment that provides an infrastructure for building applications. The applications, which exemplify some of the benefits of virtual radios, are a software cellular receiver and a novel wireless network interface.