New Camera Technology with New Challenges

Our project investigates intelligent motion detection using compressed sensing (CS), an emerging data acquisition technology with promising applications for still and video cameras. CS incorporates image compression into the initial data collection on an image rather than generating a compressed file after initially collecting a larger amount of data. By taking only as many data points as will be stored or transmitted, compressed sensing seeks to eliminate the waste from collecting many, many pixel-intensity values on an image and then using compression algorithms (such as JPEG or GIF) to encode a much smaller number of data points to closely approximate the information in the original image. [1]

Lower resource usage makes compressed sensing cameras attractive choices for low-power applications including security cameras. Ilan Goodman, Ph.D candidate at Rice University, has demonstrated that motion detection using a simulated CS camera is possible by computing entropy changes between successive CS measurements [2]. Starting from his work, we explore what can be determined about the motion of an object using compressed sensing.

[1] "Compressed Sensing Resources." Digital Signal Processing Group, Department of Electrical and Computer Engineering, Rice University. 2005. http://www.dsp.ece.rice.edu/cs.

[2] I.N. Goodman & D.H. Johnson. "Look at This: Goal-Directed Imaging with Selective Attention." (poster) 2005 Rice Affiliates Day, Rice University, 2005.