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I am working on a project wherein I am supposed to detect human beings from a live video stream which I get from a UAV's camera module. I do not need to create any rectangles or boxes around detected subjects, but just need to reply with a yes or no. I am fairly new to Open-CV and have no prior experience.
What I have tried:
I started by training my SVM on HOG features. My team gathered a few images from a UAV we had, with people in it. I then trained the SVM from the crops of those people. We got unsatisfactory results when we used the trained detector on the a video from sky with people. Moreover processing each frame turned out to be very slow , therefore the system became unusable.(it did work on still images to some extent).
My question:
I wanted to know if there is some other technique, library etc I could try for achieving good results. Please point me to the next step.
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I am trying to determine the direction of an audio signal using the microphone on an iPhone. Is there any way to do this? As far as I have read and attempted, it isn't possible. I have made extensive models with keras and even then determining the location of the sound is shaky at best due to the number of variables. So not including any ML aspects, is there a library or method to determine audio direction from an iOS microphone?
No, in general it shouldn't be possible (Even with machine learning)--you need at least two points (and excellent timing) to determine a direction. You MIGHT be able to do something with multiple iPhones, but that would require very tight timing and some learning to determine where the phones are in relation to each other--and I doubt such a library already exists for the iPhone (existing libraries could be ported/adapted though)
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I'm new to YOLO and trying to make car counting application using YOLO. The cars is from video file. Is there any reference? Thank you
There are multiple places from which I suggest you try to learn:
1) The CNN course from coursera https://www.coursera.org/learn/convolutional-neural-networks
This course has a good explanation on yolo(There assignment is on car detection as well which can easily be extended to car counting) and the rest of the course is quite nice as well
2)https://towardsdatascience.com/yolo-you-only-look-once-real-time-object-detection-explained-492dc9230006
The article focus on a few implementation details and talks about the papers yolo and yolov2 , and helped me clear a out a few issues i had when i was trying to implement yolo
3)The original paper (although this may be too advanced ): https://arxiv.org/pdf/1506.02640v5.pdf
4)A keras implmentation : https://github.com/experiencor/keras-yolo2
A simple git clone if you wish to simply have the code , although i do not recommend this as it has very little actual learning and is simply a download and use option
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I am hoping that TensorFlow can turn this input, to this output.
Input: A floorplan PNG, and 1 - 5 images of a symbol
Output: The same floorplan, but with all matching symbols highlighted
I can do the hard work of figuring out HOW to do it, but I don't want to waste 2 weeks just to figure out it wouldn't be possible. I know I'd need to train it with multiple images, but I won't have more than 5 examples of a given symbol.
Does TensorFlow have these capabilities?
Thanks!
Yes, it is possible to use tensorflow to create a machine learning algorithm to do that for you, but I would bet that is not how you want to do this. First off, in order to do this in tensorflow, you would need to manually create a large number of training samples and spend a significant amount of time figuring out how to define the network and train it. Sure, you could do it, but I definitely wouldn't advise it.
If you have a specific set of symbols that you want to highlight, it would probably be better to use opencv to find and highlight the symbols. For example, in opencv, you could use Template Matching to find a specific symbol in the floor plan and then highlight them by modifying pixel color.
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Does anyone know of or have a good tool for labeling image data to be used in training a DNN?
Specifically labeling 2 points in an image, like upperLeftCorner and lowerRightCorner, which then calculates a bouding box around the specified object. That's just an example but I would like to be able to follow the MSCoco data format.
Thanks!
You might try LabelMe, http://labelme.csail.mit.edu/Release3.0/
It's usually for outlines for segmentation, but I'm pretty sure it works fine for bounding boxes too.
I had a similar issue finding a tool that did bouding boxes for labeling image data, so I started this new project called LabelD (https://github.com/sweppner/labeld) that uses NodeJS and focuses on bouding boxes for annotation. It's still very much in alpha, but it's pretty easy to use and functional for labeling images!
Let me know if you have any questions!
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I am looking for open source Face/ Image Detection, Recognition, Video Face Mining libraries similar to the ones that were at one time available from Pittsburg Pattern Recognition. I am also interested in libraries that detect various states of facial expressions that would work on captured still images.libra I looked at OpenCV but I was not able to find a cohesive library. Also, I would be interested in getting in touch with any group that would do contract development or license available libraries. Much thanks in advance.
OpenCV has support for Face Detection and even gesture recognition, such as smile recognition, like this: auto-smiley.
openFrameworks is a good library which wraps OpenCV and makes life considerably easier, very sophisticated image processing can be done with the two.