Camera Motion Sensing

A C# desktop application that turns a webcam into a motion detector. It compares consecutive frames, sounds an alarm when movement is detected and saves images of the event.

A camera or uploaded image provides the input frame. OpenCV pre-processes it: grayscale conversion, noise removal and resizing. A detector or recognition model finds and identifies the region of interest. The result is displayed, logged in the database and can trigger an alert or action.

Capture the frame, convert to grayscale, remove noise with a Gaussian blur, segment the region of interest with thresholding or edge detection, extract features and finally classify or match them.

First a detector (Haar cascade, HOG or a CNN) finds the face. Then a model converts it into an embedding, a list of numbers that describes the face, and compares it with stored embeddings using a distance measure.

Web Forms uses server controls and view state with an event model. MVC separates Model, View and Controller, gives full control over HTML and is easier to test.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.