Real Time Abnormal Activity Detection using Machine Learning Algorithms for CCTV Surveillance System / (Record no. 611858)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02133nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240926130140.0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.382,KHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Khan, Ateeq |
| 9 (RLIN) | 126114 |
| 245 ## - TITLE STATEMENT | |
| Title | Real Time Abnormal Activity Detection using Machine Learning Algorithms for CCTV Surveillance System / |
| Statement of responsibility, etc. | Ateeq Khan, Iman Abid, Habib Hussain, Ali Raza. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | MCS, NUST |
| Name of publisher, distributor, etc. | Rawalpindi |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 49 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | The 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | UG EE Project |
| 9 (RLIN) | 118090 |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME | |
| Geographic name | BEE-57 |
| 9 (RLIN) | 125983 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Abdul Wakeel |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Project Report |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | General Stacks | 09/26/2024 | 621.382,KHA | MCSPTC-472 | 09/26/2024 | 09/26/2024 | Project Report |