Abstract: Real time video processing is a
requirement for applications like self
driving cars, however this concept can
be further explored for applications like
video surveillance monitoring. Such
applications can be made more
efficient by applying optimizations
common in scientific computing to
vision pipelines. This work presents a
case study of optimizing steps in a
Computer Vision pipeline. The case-
study is a real world background
subtraction algorithm called ViBe on an
NVIDIA developed JETSON TK1 GPU.

This case study provides evidence that
further stages of a vision pipeline are
possible in real time.
Data Movement is a major bottleneck
in many applications.
In this work we use a small,
inexpensive GPU processor like the
JETSON and place it close to the
camera. Thus we attempt to solve the
network traffic latency problem. The
optimizations for this algorithm aim to
reduce memory traffic by using data
decomposition plans.The simultaneous
use of GPU and CPU capability
reduces overall execution time, and
therefore latency. The optimization
space for ViBe has been explored to
achieve real time performance.

With these optimizations we achieved
a frame rate of 55.33 fps placing us
well within the real time threshold of 30
fps for real time processing of video.
Thus we laid the foundation to a
starting stage of an optimized
Computer Vision pipeline. We also
achieved power efficient optimization
with the use of the capabilities of the
JESTON TK1.

Adviser: Prof. Ross Beveridge
Co-Adviser: Special Assistant Prof. Catherine Olschanowsky
Non-ECE Member: Prof. Stephen Guzik, Department of Mechanical Engineering
Member 3: Prof. Mahmood Azimi Sadjadi
Addional Members: N/A

Publications:
N/A

Program of Study:
ECE512
ECE513
ECE514
ECE530
ECE699
CS475
CS510
CS545