Introduction to embedded vision and the OpenCV library
The Optical Flow Example
Optical flow estimates motion by analyzing how groups of pixels in the current frame changed position from the previous frame of a video sequence (Figure 4 below).
The "group of pixels" is a feature. Optical flow estimation finds use in predicting where objects will be in the next frame. Many optical flow estimation algorithms exist; this particular example uses the Lucas-Kanade approach. The algorithm's first step involves finding "good" features to track between frames. Specifically, the algorithm is looking for groups of pixels containing corners or points.
Click on image to enlarge.
Click on image to enlarge.
The qlevel variable determines the quality of a selected feature. Consistency is the end objective of using a lot of math to find quality features.
A "good" feature (group of pixels surrounding a corner or point) is one that an algorithm can find under various lighting conditions, as the object moves. The goal is to find these same features in each frame.
Once the same feature appears in consecutive frames, tracking an object is possible. The lines in the output video represent the optical flow of the selected features.
The MaxCount parameter determines the maximum number of features to look for. The minDist parameter sets the minimum distance between features. The more features used, the more reliable the tracking.
The features are not perfect, and sometimes a feature used in one frame disappears in the next frame. Using multiple features decreases the chances that the algorithm will not be able to find any features in a frame.
Variables:
* MaxCount: The maximum number of good features to look for in a frame.
* qlevel: The acceptable quality of the features. A higher quality feature is more likely to be unique, and therefore to be correctly identified in the next frame. A low quality feature may get lost in the next frame, or worse yet may be confused with another feature in the image of the next frame.
* minDist: The minimum distance between selected features.
The Face Detector example
The face detector used in this example is based on the Viola-Jones feature detector algorithm (Figure 5 below). Throughout this article, we have been working with different algorithms for finding features; i.e. closely grouped pixels in an image or frame that are unique in some way.
The motion detector used subtraction of one frame from the next frame to find pixels that moved, classifying these pixel groups as features. In the line detector example, features were groups of edge pixels organized in a straight line. And in the optical flow example, features were groups of pixels organized into corners or points in an image.
Click on image to enlarge.
Click on image to enlarge.
The Viola-Jones algorithm uses a discrete set of six Haar-like features (the OpenCV implementation adds additional features). Haar-like features in a 2D image include edges, corners, and diagonals. They are very similar to features in the optical flow example, except that detection of these particular features occurs via a different method.
As the name implies, the face detector example detects faces. Detection occurs within each individual frame; the detector does not track the face from frame to frame.
The face detector can also detect objects other than faces. An XML file "describes" the object to detect. OpenCV includes various Haar cascade XML files that you can use to detect various object types. OpenCV also includes tools to allow you to train your own cascade to detect any object you desire and save it as an XML file for use by the detector.
Variables:
* MinSize: The smallest face to detect. As a face gets further from the camera, it appears smaller. This parameter also defines the furthest distance a face can be from the camera and still be detected.
* MinN: The “minimum neighbor” parameter combines, into a single detection, faces that are detected multiple times. The face detector actually detects each face multiple times in slightly different positions. This parameter simply defines how to group the detections together. For example, a MinN of 20 would group all detection within 20 pixels of each other as a single face.
* ScaleF: Scale factor determines the number of times to run the face detector at each pixel location. The Haar cascade XML file that determines the parameters of the to-be-detected object is designed for an object of only one size.
In order to detect objects of various sizes (faces close to the camera as well as far away from the camera, for example) requires scaling the detector.
This scaling process has to occur at every pixel location in the image. This process is computationally expensive, but a scale factor that is too large will not detect faces between detector sizes.
A scale factor too small, conversely, can use a huge amount of CPU resources. You can see this phenomenon in the example if you first set the scale factor to its max value of 10. In this case, you will notice that as each face moves closer to or away from the camera, the detector will not detect it at certain distances.
At these distances, the face size is in-between detector sizes. If you decrease the scale factor to its minimum, on the other hand, the required CPU resources skyrocket, as shown by the prolonged detection time.
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