Recent development on compressive sensing (CS) presents a great potential for this technique to be used in broader applications from hyper-spectroscopy microscopy to homeland security. And the new mathematics of CS has drastically benefited this field especially in imaging and video applications. Based on novel theoretical principles and experiments, it has been demonstrated that an image can be reconstruct with only K << N measurements from an N-dimensional basis, which is much less than the sampling rate required by the Shannon-Nyquist sampling theorem. The compressive single pixel camera is one embodiment of such an imaging system and has proven capable of capturing both static images and dynamic scenes using fewer measurements than the current schemes. In this thesis we will explore compressive dynamic scene acquisition with prior information or models, incorporating with different sensing matrixes. We demonstrate through simulations and experiments the effectiveness of knowledge-enhanced patterns over unbiased compressive measurements in a variety of applications including motion tracking and object recognition. We also present using a SPC like system for high-speed anomaly detection. Despite its importance in a wide variety of machine vision applications, extending anomaly detection and tracking beyond the visible spectrum in a cost-effective manner presents a significant technological challenge. As a step in this direction, we present a compressive imaging system, specially designed patterns, and a set of metrics to identify the existence of short durance anomalies against a complex background. Our novel measurement design is chosen to be most sensitive to singular anomalies based on the Walsh-Hadamard transform. We illustrate the utility of our approach via a series of simulations and experiments on the compressive single-pixel camera system.