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updated Abstract, Implementation, and Conclusion

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1 parent 1cf51b9 commit 920a9b7e76b7bdf44ef164eaad19a7d5c751740f @shinpei0208 committed Mar 28, 2013
Showing with 20 additions and 7 deletions.
  1. +6 −6 draft/abstract.tex
  2. +9 −0 draft/conclusion.tex
  3. BIN draft/draft.pdf
  4. +5 −1 draft/implementation.tex
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@@ -1,17 +1,17 @@
Visione-based object detection using camera sensors is an essential piece
of perception for autonomous vehicles.
Various combinations of features and models can be applied to increase
-the quality and speed of object detection.
+the quality and the speed of object detection.
A well-known approach uses histograms of oriented gradients (HOG)
-with deformable models to detect a car in an image.
+with deformable models to detect a car in an image \cite{Niknejad12}.
A major challenge of this approach can be found in computational cost
-introducing a real-time constraint problem in the real world.
+introducing a real-time constraint relevant to the real world.
In this paper, we present an implementation technique using graphics
processing units (GPUs) to accelerate computations of scoring similarity
of the input image and the pre-defined models.
-Our implementation considers not only the algorithm part but also the
-entire program structure for practical use.
-We also apply the presented technique for the real-world car detection
+Our implementation considers the entire program structure as well as the
+specific algorithm for practical use.
+We apply the presented technique to the real-world vehicle detection
program and demonstrate that our implementation using commodity
GPUs can achieve speedups of 1.5x to 3x in frame-rate over sequential
and multithreaded implementations using traditional CPUs.
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@@ -18,6 +18,15 @@ \section{Conclusion}
our contribution is useful and significant for real-world applications
of vision-based object detection.
+To the best of our knowledge, this is the first piece of work that made
+a \textit{tight} coordination of object detection and parallel computing
+-- a core challenge of CPS.
+Specifically we showed that a measured and structured way of GPU
+programming is efficient for the object detection program and quantified
+the impact of GPUs in performance.
+Our conclusion is that GPUs are promising to meet the required
+performance of vision-based object detection in the real world.
+
In future work, we plan to complement this work with systemized
coordinations of computations and I/O devices.
Since real-world applications require camera sensors to obtain input
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@@ -148,4 +148,8 @@ \subsection{Implementation Approach}
may further improve performance but such a fine-grained performance
tuning is outside the scope of this paper.
-\subsection{GPU Programming}
+\subsection{GPU Programming}
+
+Once computational blocks of the program to be parallelized are
+determined, we can focus on the program structure rather than the
+context to implement the program using the GPU.

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