I am using this example from a Computer Vision Made Easy" Matlab Webinar I watched, since I intend to use Computer Vision for my research in order to count cars and/or other types of vehicles.
Although I have changed some of the filter parameters and the detection works quite well, the problem is that the script displays ALL moving objects in the video. I would like to count vehicles from a specific road but my video screenshot includes many roads (screenshot here).
1) Is there a way to set the area of the video that I would like to detect cars? For example, only the "green arrow" road, and leave out the rest? I tried to crop the video but it is not a good solution since a part of another road always appears(screenshot here).
2) Moreover, in which part of the code can I add a counter in order to have an output on how many vehicles passed through the specific segment of the road? Any ideas on that?
If you know ahead of time where the road is, you can create a binary mask image, where the road is marked with 1's, and everything else has the value of 0. Then you can simply check whether or not a moving object is inside your region of interest.
Once you get comfortable with this example, check out a more advanced version, which not only detects moving objects, but also tracks them using the Kalman filter.
Related
I was wondering if someone can provide me a guideline to detect if a person in a picture is bald or not, or even better, how much hair s\he has.
So far I tried to detect the face and the eyes position. From that information, I roughly estimate the forehead and bald area by cutting the area above the eyes as high as some portion of the face.
Then I extract HOG features and train the system with bald and not-bald images using SVM.
Now when I'm looking at the test results, I see some pictures classified as bald but some of them actually have blonde hair or long forehead that hair is not visible after the cutting process. I'm using MATLAB for these operations.
So I know the method seems to be a bit naive, but can you suggest a way of finding out the bald area or extracting the hair, if exists. What method would be the most appropriate for that kind of problem?
very general, so answer is general unless further info provided
Use Computer Vision (e.g MATLAB Computer Vision toolkit) to detect face/head
head has analogies (for human faces), using these one can get the area of the head where hair or baldness is (it seems you already have these)
Calculate the (probabilistic color space model) range where the skin of the person lies (most peorple have similar skin collor space range)
Calculate percentage of skin versus other color (meaning hair) in that area
You have it!
To estimate a skin color model check following papers:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.8637&rep=rep1&type=pdf
http://infoscience.epfl.ch/record/135966
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569293757.pdf
Link
If an area does not fit well with skin model it can be taken as non-skin (meaning hair, assuming no hats etc are present in samples)
Head region is very small, hence, using HOG for classification doesn't make much sense.
You can use prior information - like detect faces; baldness/hair is certain to be found on the area above the face. Also, use some denser feature descriptors.
You are probably ending up with very sparse representation or equivalently less information because of which your classifier is not able to classify correctly.
I am working on a drone based video surveillance project. I am required to implement object tracking in the same. I have tried conventional approaches but these seem to fail due to non static environment.
This is an example of what i would want to achieve. But this uses background subtraction which is impossible to achieve with a non static camera.
I have also tried feature based tracking using SURF features, but it fails for smaller objects and is prone to false positives.
What would be the best way to achieve the objective in this scenario ?.
Edit : An object can be anything within a defined region of interest. The object will usually be a person or a vehicle. The idea is that the user will make a bounding box which will define the region of interest. The drone now has to start tracking whatever is within this region of interest.
Tracking local features (like SURF) won't work in your case. Training a classifier (like Boosting with HAAR features) won't work either. Let me explain why.
Your object to track will be contained in a bounding box. Inside this bounding box there could be any object, not a person, a car, or something else that you used to train you classifier.
Also, near the object, in the bounding box there will be also background noise that will change as soon as your target object moves, even if the appearance of the object doesn't change.
Moreover the appearance of you object changes (e.g. a person turns, or drop the jacket, a vehicle get a reflection of the sun, etc...), or the object gets (partially or totally) occluded for a while. So tracking local features is very likely to lose the tracked object very soon.
So the first problem is that you must deal with potentially a lot of different objects, possibly unknown a priori, to track and you cannot train a classifier for each one of these.
The second problem is that you must follow an object whose appearance may change, so you need to update your model.
The third problem is that you need some logic that tells you that you lost the tracked object, and you need to detect it again in the scene.
So what to do? Well, you need a good long term tracker.
One of the best (to my knowledge) is Tracking-Learning-Detection (TLD) by Kalal et. al.. You can see on the dedicated page a lot of example videos, and you can see that it works pretty good with moving cameras, objects that change appearance, etc...
Luckily for us, OpenCV 3.0.0 has an implementation for TLD, and you can find a sample code here (there is also a Matlab + C implementation in the aforementioned site).
The main drawback is that this method could be slow. You can test if it's an issue for you. If so, you can downsample the video stream, upgrade your hardware, or switch to a faster tracking method, but this depends on you requirements and needs.
Good luck!
The simplest thing to try is frame differencing instead of background subtraction. Subtract the previous frame from the current frame, threshold the difference image to make it binary, and then use some morphology to clean up the noise. With this approach you typically only get the edges of the objects, but often that is enough for tracking.
You can also try to augment this approach using vision.PointTracker, which implements the KLT (Kanad-Lucas-Tomasi) point tracking algorithm.
Alternatively, you can try using dense optical flow. See opticalFlowLK, opticalFlowHS, and opticalFlowLKDoG.
I am doing research on touch screens and I couldnot find a good source except for this image below which could explain how multitouch IR systems work. basically the single touch IR systems are pretty simple as on two sides of the panel, lets say left and top are the IR transmitters and on the right and bottom are the receivers. So if a user touches somewhere in the middle, the path of IR will be disrupted and the ray will not reach the receiving end, therefore the processor can pick up the coordinates. but this will not work for multitouch systems as there is an issue of ghost points with this approach.
Below I have an image of 'PQ labs' multitouch IR system working, but as there is no explanation given, therefore I am not able to understand its working, Any help will be greatly appreciated.
I consider that they have a special algorithm to avoid the point caused by the inner cross of emitter light. But this algorithm will not work for every time, so sometime if you put your finger very close to each other. The ghost point may will show up.
My guess:
The sensors are analog (there must be an Analog to digital converter to read each of the opto transistor (IR receiver).
LEDa and LEDb are not on at the same time
The opto transistor are running in a linear range (not in saturation) when no object is present.
One object:
4. When an One object is placed on the surface. There will be less light accessing some of the opto transistors. This will be reflected by a reading that is lower then the read when no object is present.
The reading of the photo transistor array (it is an array reflecting the read from each opto transistor) will provide information about:
4.1. How many opto transistors are completely shaded (off)
4.2. What opto transistor are effected
Please note: A reading from one LED is not sufficient to know the object position
To get object location we need two reading (one from LEDa and one from LEDb). Now, we can calculate the object Position and Size since we know the geometry of the screen.
Two Objects:
Now each array may have "holes" (there will be two groups) in the shaded area. These holes will indicate that there is an additional object.
If the objects are closed to each other the holes may not be seen. However, there are many LEDs. So there will be multiple arrays (one for each LED) and based on the presented geometry these holes may be seen by some of the LEDs.
For more information please see US patent#: US7932899
Charles Bibas
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Object recognition system using matlab
I need help to develop an object recognition system. It needs to identify an object in an image by comparing it with an image in an existing database. For example my database may consist of images of cars, buses, cups, etc. If i give a certain image as an input i want the code to check and tell me whether a car(as in the car in the database) can be found to exist in the input image or not. This is strictly to be implemented in matlab. I have tried correlation, image subtraction and a few other algorithms but to no effect. Thanks in advance.
This is a complex subject, that is really on the bleeding edge of technology, but let me give you a few pointers to help start things out.
Somehow, you need to take into account the different sizes, angles, etc that might be around. A car looks very different if photographed from a few feet away as compared to 50 feet, as would it photographed from the front vs the side.
Edge detection algorithms generally work well at pulling the target object's shape away. Take the edges, identify lines in them, and you can try to compare these lines with those from your model.
Range to objects really makes a huge difference in building a successful algorithm. If you know the difference from the front of the car to the back, it can make all of the difference in the world.
Focus, noise, lighting, etc need to somehow be dealt with, to ensure that the system works well.
All in all, I would recommend taking some image analysis classes, reading several papers on the subject, or at least reading the Wikipedia Article, and then starting to work on your project.
The problem you have described is sometimes called object category recognition or object class recognition to emphasize that you are not trying to recognize a particular object, but a member of a category such as "car" or "person".
One popular approach for solving this problem is called Bag of Features of "Bag of Words". If you have access to the Computer Vision System Toolbox for Matlab, it has functions for detecting SURF features, which can be used for this approach.
Also, a better place to ask this question might be Signal and Image Processing stack exchange.
I would like to track (if that is the right word for this) the movement of a point on an object and return the co-ordinates for the point in each frame to arrays for plotting. How would you go about doing this?
The point on the video is a certain color and so my first effort was to eliminate all other colors and change the part I wish to follow to black and everything else to white. Doing this left me with some areas in the background which are the same color but I wish to ignore them and just focus on the moving point. I do not know where to even begin with this or if I've even been trying to do the right thing so far?
Any help would be greatly appreciated! :)
Try searching for terms like 'tracking', 'morphological', 'computer vision', 'matlab'
Here's a project that I found that will probably get you started.
http://www.mathworks.com/matlabcentral/fileexchange/28757-tracking-red-color-objects-using-matlab
if your object of interests is of a certain specific color. You can always apply a color-filter. To give you a bit of a background, i was trying to track not a point on an object, but a moving object in one of the videos i have. (it was a ping-pong video and my goal was to track the ping-pong ball). My algorithm was simple and fast (as i did not want any of my filters to induce heavy computations at one single frame). The basic idea was to apply a color filter. Similar to other shape filters, if your target is of high similarity to the filter, the response will be distinctive enough for you to notice. In other words, if you minus two objects that are extremely similar, you will get 0, otherwise, it will be far greater than 0.