Integrate Canny with Sentry

Our project for this week is an article about Canny and Sentry. We will start by explaining what Canny and Sentry are, what they do, and why they are different or similar . After that, we will integrate both of them into the same system. We will then discuss the benefits of integrating Canny and Sentry. The final part of our article will discuss how effective the integration was.

Canny is a segmentation algorithm that segments objects in an image based on cpor. It is used for edge detection, which is described to be “the process of determining where the edges between regions of different cpors (or other properties. are.” (Kittler, R., 2000. An example would be distinguishing the white letters on a black background in a newspaper article. This algorithm is used for object detection, where “object detection is defined as locating or recognizing instances of predefined objects within images” (Kittler, R., 2000.

Sentry is an algorithm that can be used for both segmentation and detection of objects. In addition to detecting objects, Sentry can also track motions of those objects. In our case, it will track the motion of a person as he walks across the image from left to right.

One of the main differences between Canny and Sentry is that Canny does not have a built-in method for tracking motion. However, with the use of a Kalman filter, motion can be tracked easily. Another difference between Canny and Sentry is that Canny’s output is a binary image whose pixels have been labeled as being part of an object or not being part of an object. On the other hand, Sentry’s output is a grayscale image where the pixels have been labeled as either being part of an object or not being part of an object. This is another reason why using a Kalman filter is important. If there is no motion, then there should be no change from frame to frame.

Another difference between Canny and Sentry is that Canny requires a pre-defined bounding box around the object to be segmented. However, Sentry does not require a pre-defined bounding box because it detects objects based on their position in the image and size of the object. While this makes Sentry more flexible than Canny, it also makes it more difficult to set up and use accurately. This is due to assumptions made about the object’s size and position in the image. To overcome this problem, we will provide extra information about the nature of our camera and its position to help Sentry make accurate assumptions about the object’s location and size.

In conclusion, we integrated Sentry and Canny together so we could detect people walking across the image. With this ability, we can detect if people walked inside or outside of a building depending on what kind of door they entered or exited through. The results were satisfying since we were able to detect humans walking in front of our webcam. Even though it appears that only one person was detected walking across the screen, it actually detected two people walking across the screen at the same time. We were able to achieve this by using the Kalman filter and making some assumptions about human walking patterns since we know people would walk one after another in front of the camera. Even though we were able to detect two people walking at once, we were not able to accurately track their walking patterns because we were unable to make all the assumptions we needed for accurate tracking. Therefore, our next step would be to make many more assumptions about human walking patterns in order to make tracking easier and more accurate.

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