000			02133nam a22001817a 4500
003			NUST
005			20240926130140.0
082			_a621.382,KHA
100			_aKhan, Ateeq _9126114
245			_aReal Time Abnormal Activity Detection using Machine Learning Algorithms for CCTV Surveillance System / _cAteeq Khan, Iman Abid, Habib Hussain, Ali Raza.
260			_aMCS, NUST _bRawalpindi _c2024
300			_a49 p
505			_aThe demand for security and reassurance to the public has always been the foremost concern of the society. Anomaly detection plays a prime role in surveillance applications. It is one of many enabling technologies for increasing security, enabling law enforcement and other security personnels to instantly respond to potential threats. In this thesis, the issue of identifying any abnormal event in the surveillance domain has been studied, with a literature review that identifies some weaknesses in previous anomaly detection techniques. Constant observation of surveillance cameras generating sheer volume of data by humans is merely unfeasible. This inconvenience is creating a dire need to accurately automate the entire process. When threats are definable, we can use methods based on situation recognition to detect them, but sometimes the anomalies are hard to define. In such cases a technique called data-driven anomaly detection is applied. In data driven anomaly detection a model of normalcy is trained and utilized to find anomalies. Anomalous activities are then alarmed providing instant intervention and prevention of criminal activity dispensing an effective and efficient surveillance and reducing the labor of constant monitoring. We intend to utilize Deep Learning (DL) model: “Autoencoders” to identify and classify activities from a batch of 10 video frames. The evaluation of proposed solution is done on data sets, particularly for corridor and indoor settings. Conclusion of the thesis is that the proposed model is strong and suitable for use.
650			_aUG EE Project _9118090
651			_aBEE-57 _9125983
700			_aSupervisor Dr. Abdul Wakeel
942			_2ddc _cPR
999			_c611858 _d611858