How am I able to speed up the sliding window in Matlab for an operational object recognition?

1
votes

I'm working on a high resolution data set for extracting desired image objects. To do this, I have applied HOG + SVM. Although the results I get are very promising and operationally accepted, the speed of this task is annoying! For each image, which is of size 2800 * 1400, the algorithm takes 4 minutes to be done, which is far from the time I need. Most of this time is taken for performing sliding window. I need this recognition procedure to be done within only 10 seconds. Since I make the use of Matlab, it seems that reaching 10 seconds is impossible and I have to think of C++. However, I've never worked with C++ and haven't enough time to learn it for this task.

I would be grateful to know that is there any ways to speed up the sliding window in Matlab or Python to perform the recognition in under 1 minute?

1
pythonmatlabmatlab-cvstobject-recognitionsliding-window
It may be worth posting the code that you have on the Code Review site for suggestions on optimization. – Suever
Not only will you need to use a language like C++ to improve efficiency, but you'll probably need to use CUDA to offload a majority of the computational overhead onto GPUs. I believe there are CUDA extensions/libraries for Python as well. Doing it in Matlab with only the use of a CPU will not get you even close to under a minute, let alone under 10 seconds. – Scott Mudge
Many thanks to both of you for your helpful comments. @Scott, if there exists CUDA modules for Python, is it possible to get the goal without employing C++? – Federico
Depending on the performance of the GPUs available to CUDA and the efficiency of your algorithms, certainly. – Scott Mudge
If you have the parallel computing toolbox, you also have CUDA support in Matlab. – Jonas

1 Answers

2
votes

Does it have to be HOG-SVM?

If not, then you can use the trainCascadeObjectDetector function in the Computer Vision System Toolbox. It trains a boosted cascade classifier, and it gives you a choice of HOG, LBP, and Haar features. Then you can use vision.CascadeObjectDetector, which implements the sliding windows internally using native code, making it much faster.

See matlab documentation on training a cascade object detector.