Cameras in Embedded Systems
smart camera performs real-time analysis to recognize scenic elements. Smart cameras
are useful in a variety of scenarios: surveillance, medicine, etc.We have built
a real-time system for recognizing gestures. Our smart camera uses novel algorithms
to recognize gestures based on low-level analysis of body parts as well as hidden
Markov models for the moves that comprise the gestures. These algorithms run on
a Trimedia processor. Our system can recognize gestures at the rate of 20 frames/second.
The camera can also fuse the results of multiple cameras
Recent technological advances are enabling a new generation of smart cameras that
represent a quantum leap in sophistication. While today's digital cameras capture
images, smart cameras capture high-level descriptions of the scene and analyze
what they see. These devices could support a wide variety of applications including
human and animal detection, surveillance, motion analysis, and facial identification.
Video processing has an insatiable demand
for real-time performance. Fortunately, Moore's law provides an increasing pool
of available computing power to apply to real-time analysis. Smart cameras leverage
very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power
system with substantial memory. Moving well beyond pixel processing and compression,
these systems run a wide range of algorithms to extract meaning from streaming
Because they push the design space
in so many dimensions, smart cameras are a leading-edge application for embedded
Detection and Recognition
Although there are many approaches to real-time video analysis,
we chose to focus initially on human gesture recognition-identifying whether a
subject is walking, standing, waving his arms, and so on. Because much work remains
to be done on this problem, we sought to design an embedded system that can incorporate
future algorithms as well as use those we created exclusively for this application.
algorithms use both low-level and high-level processing. The low-level component
identifies different body parts and categorizes their movement in simple terms.
The high-level component, which is application-dependent, uses this information
to recognize each body part's action and the person's overall activity based on
The system captures images from the video input, which can be either uncompressed
or compressed (MPEG and motion JPEG), and applies four different algorithms to
detect and identify human body parts.
extraction: The first algorithm transforms the pixels of an image into an
M ¥ N bitmap and eliminates the background. It then detects the body part's
skin area using a YUV color model with chrominance values down sampled
algorithm hierarchically segments the frame into skin-tone and non-skin-tone regions
by extracting foreground regions adjacent to detected skin areas and combining
these segments in a meaningful way.
following: The next step in the process involves linking the separate groups
of pixels into contours that geometrically define the regions. This algorithm
uses a 3 ¥ 3 filter to follow the edge of the component in any of eight different
Ellipse fitting: To
correct for deformations in image processing caused by clothing, objects in the
frame, or some body parts blocking others, an algorithm fits ellipses to the pixel
regions to provide simplified part attributes. The algorithm uses these parametric
surface approximations to compute geometric descriptors for segments such as area,
compactness (circularity), weak perspective invariants, and spatial
Graph matching: Each extracted region
modeled with ellipses corresponds to a node in a graphical representation of the
A piecewise quadratic Bayesian classifier uses the ellipses parameters
to compute feature vectors consisting of binary and unary attributes. It then
matches these attributes to feature vectors of body parts or meaningful combinations
of parts that are computed offline. To expedite the branching process, the algorithm
begins with the face, which is generally easiest to detect.
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