Group: 2014-FYP-23



Dr. Kashif Javed


Intelligent Discipline Maintenance System monitors non-disciplinary actions like door slamming, whistling, screaming, smoking and garbage throwing. The system is divided into two major parts. One is responsible for audio processing and other for video processing. The audio detection and recognition system receives real-time audio from a microphone, use standardizing procedures like Median Filters to normalize it, extract its features and form a feature matrix for that sound sample. The extracted features are Zero Crossing Rate (ZCR), Short Time Energy (STE) and Mel Frequency Cepstral Coefficients (MFCCs). In next stage, Principal Component Analysis (PCA) is used to extract more descriptive features and reduce dimensionality of the feature matrix. Finally, Support Vector Machine (SVM) using Gaussian kernel is trained and later used to recognize sounds like door slamming, whistling and screaming. The video detection and recognition system detects smoking and garbage throwing using image processing and machine learning. It receives real-time video data and detects moving objects in the captured video frame using background estimation method. Template matching is performed to detect garbage or cigarette in the captured frame. In case of smoking event detection, persons face is also detected using Haar Classifier. The smoke detection is carried out and the event is declared as smoking or not smoking accordingly. When some non-disciplinary action is detected, the system automatically reports the authority to take further actions.

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Our Vision

To ensure understanding and application of engineering fundamentals to address social needs.

Our Mission

To become a center of excellence in knowledge creation and dissemination by inculcating analysis and design skills in electrical engineering students.


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