Example 1. Detects motion by comparing each video frame to the previous frame. The change is visualized and is calculated as a number. Example 2. Detects the presence of someone or something in front of the camera by comparing each video frame <strong>with</strong> a previously saved frame. The change is visualized and is calculated as a number. Example 3. Distinguishes the silhouette of people or objects in each video frame by comparing each pixel to a threshold value. The circle is fi lled <strong>with</strong> white when it is <strong>with</strong>in the silhouette. Example 4. Tracks the brightest object in each video frame by calculating the brightest pixel. The light from the fl ashlight is the brightest element in the frame; therefore, the circle follows it. 551 Extension 3: Vision
Example 4: Brightness tracking (p. 560) A rudimentary scheme for object tracking, ideal for tracking the location of a single illuminated point (such as a fl ashlight), fi nds the location of the single brightest pixel in every fresh frame of video. In this algorithm, the brightness of each pixel in the incoming video frame is compared <strong>with</strong> the brightest value yet encountered in that frame; if a pixel is brighter than the brightest value yet encountered, then the location and brightness of that pixel are stored. After all of the pixels have been examined, then the brightest location in the video frame is known. This technique relies on an operational assumption that there is only one such object of interest. With trivial modifi cations, it can equivalently locate and track the darkest pixel in the scene, or track multiple and differently colored objects. Of course, many more software techniques exist, at every level of sophistication, for detecting, recognizing, and interacting <strong>with</strong> people and other objects of interest. Each of the tracking algorithms described above, for example, can be found in elaborated versions that amend its various limitations. Other easy-to-implement algorithms can compute specifi c features of a tracked object, such as its area, center of mass, angular orientation, compactness, edge pixels, and contour features such as corners and cavities. On the other hand, some of the most diffi cult to implement algorithms, representing the cutting edge of computer vision research today, are able (<strong>with</strong>in limits) to recognize unique people, track the orientation of a person’s gaze, or correctly identify facial expressions. Pseudocodes, source codes, or ready-to-use implementations of all of these techniques can be found on the Internet in excellent resources like Daniel Huber’s Computer Vision Homepage, Robert Fisher’s HIPR (Hypermedia Image <strong>Processing</strong> Reference), or in the software toolkits discussed on pages 554-555. Computer vision in the physical world Unlike the human eye and brain, no computer vision algorithm is completely general, which is to say, able to perform its intended function given any possible video input. Instead, each software tracking or detection algorithm is critically dependent on certain unique assumptions about the real-world video scene it is expected to analyze. If any of these expectations are not met, then the algorithm can produce poor or ambiguous results or even fail altogether. For this reason, it is essential to design physical conditions in tandem <strong>with</strong> the development of computer vision code, and to select the software techniques that are most compatible <strong>with</strong> the available physical conditions. Background subtraction and brightness thresholding, for example, can fail if the people in the scene are too close in color or brightness to their surroundings. For these algorithms to work well, it is greatly benefi cial to prepare physical circumstances that naturally emphasize the contrast between people and their environments. This can be achieved <strong>with</strong> lighting situations that silhouette the people, or through the use of specially colored costumes. The frame-differencing technique, likewise, fails to detect people if they are stationary. It will therefore have very different degrees of success 552 Extension 3: Vision
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Using Processing Download, Install
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Sketch_123.java The JAVA fi le gene
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