The paper proposes a workflow for the automatic detection of anomalous behaviour in an examination hall, towards the automated proctoring of tests in classes. Certain assumptions about normal behaviour in the context of proctoring exams are made. Anomalies are behaviour patterns that are relatively (and significantly) different. While not every anomalous behaviour may be cause for suspicion, the system is designed to detection typical patterns for actions of concern such as discussions during an exam or the turning around or the passing of notes, etc. This detection is based on features computed using the textural features followed by a classifier search through annotated patterns of pre- recorded clips to train the system for behaviour that may cause concern. While there may be false positives, the system is intended as a decision support system to facilitate automatic proctoring of tests and deters malpractice. We have discussed about various video analytics or video processing and image processing methods and tools involved in surveillance model. We throughout the paper have walked through about the various processes- pre-processing segmentation, classification, feature extraction and its related video processing algorithm in sequential manner. The proposed model is effective, efficient and requires relatively less processing power.
The major problem that occurs in examination system is malpractices. This is identified due to the absence of credible identity verification system for offline and also for online examinations. In order to overcome the above problem researchers have focused on the use of artificial techniques and use of biometrics. In the past history work has been carried out on examination malpractices. Examination malpractice is any form of an illegal act committed during the examination. There are different forms of examination malpractice including copying from another student?s test, getting notes to examination, plagiarism and impersonating another student during a test. All these scenarios and many others give students an unfair advantage. There may be many factors that cause examination malpractice like physiological factors, societal value system, over emphasis on paper qualification and poor learning facilities.
- Fuzzy clustering
- Otsu method
- KNN classifier algorithm
- Approximate result at the regulation of speed and direction
- It cannot work on the problems of scattering and at non co-ordinate system
- Haar-Features Algorithm
- AdaBoost Algorithm
- Principal Component Analysis(PCA)
- Concept is easy to understand
- optimization Good for ?noisy? environments
- It support multi objective
Image Pre-processing is a common name for operations with images at the lowest level of abstraction. The pre-processing is the initial step for the process of video frames; it includes conversion of image frames into grayscale image. The pre-processing of input video is done to remove the noise and outliers, which are performed by Gaussian Filter. Its input and output are intensity images. The aim of pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image features important for further processing. Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image. Corruption may come in many forms such as motion blur, noise, and camera misfocus. Image Pre-processing is a common name for operations with images at the lowest level of abstraction. Its input and output are intensity images. The aim of pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image features important for further processing.
- Ada Boost, short for ?Adaptive Boosting?, is the first practical boosting algorithm proposed by Freund and Schapiro in 1996. It focuses on classification problems and aims to convert a set of weak classifiers into a strong one. The final equation for classification can be represented as where fem. stands for the math weak classifier and theta is the corresponding weight.?
Ada-boost or Adaptive Boosting combines multiple classifiers to increase the accuracy of classifiers. AdaBoost is an iterative ensemble method. AdaBoost classifier builds a strong classifier by combining multiple poorly performing classifiers so that you will get high accuracy strong classifier. The basic concept behind Ad boost is to set the weights of classifiers and training the data sample in each iteration such that it ensures the accurate predictions of unusual observations. Any machine learning algorithm can be used as base classifier if it accepts weights on the training set. Adaboost should meet two conditions: The classifier should be trained interactively on various weighed training examples. In each iteration, it tries to provide an excellent fit for these examples by minimizing training error. Boosting is a general ensemble method that creates a strong classifier from a number of weak classifiers. This is done by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. Models are added until the training set is predicted perfectly or a maximum number of models are added. Adaboost was the first really successful boosting algorithm developed for binary classification. It is the best starting point for understanding boosting.
Background subtraction, also known as?foreground detection, is a technique in the fields of?image processing?and?computer vision?wherein an image’s foreground is extracted for further processing (object recognition etc.). Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called “background image”, or “background model”. Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation.
- 4 GB of RAM
- 500 GB of Hard disk
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 Dehghani, Alireza, and Alistair Sutherland. “A novel interest-point-based background subtraction algorithm.” ELCVIA Electronic Letters on Computer Vision and Image Analysis 13.1 (2014).
 Ben-Musa, Ahmad Salihu, Sanjay Kumar Singh, and Prateek Agrawal. “Suspicious Human Activity Recognition for Video Surveillance System.” (2014).
 Yong, Lu, and He Dongjian. “Video-based detection of abnormal behavior in the examination room.” Information Technology and Applications (IFITA), 2010 International Forum on. Vol. 3. IEEE, 2010.