Classification of Live Video Stream from Pakistani News Channels (Urdu) using Deep Learning Latest Techniques / (Record no. 607398)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02892nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Afzal, Muhammad |
| 245 ## - TITLE STATEMENT | |
| Title | Classification of Live Video Stream from Pakistani News Channels (Urdu) using Deep Learning Latest Techniques / |
| Statement of responsibility, etc. | Muhammad Afzal |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 134p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | In our contemporary era, information is of prime importance and its dominant use by<br/>social media and TV channels for making public opinion and cultural influence is quite evident.<br/>Videos form the major portion of media and contain more elaborate information than a single<br/>image. Today, videos are piling up in millions every day and their segregation, classification and<br/>analysis are upheaval tasks. Live TV video stream contains voice, metadata and image frames<br/>full of multiple information including written scripts etc. which can contribute to video<br/>classification. But utilization of each type of data we need to do a separate study. However, we<br/>have focused on classification of video stream using deep learning (DL) neural networks which<br/>are well established solutions for images and small videos classification and gesture recognition.<br/>In our study, we have suggested a mechanism for classification of big or live video<br/>streams obtained from Pakistani TV News Channels into 5 classes (Advertisement, News, Talk<br/>Show, Sports & Entertainment Program) using supervised DL pretrained neural networks. Due to<br/>non-availability of authentic dataset on this subject, we have created a customized data of videos<br/>recorded (approximately 335 hours videos) from various sources like different TV channels‘<br/>websites and YouTube. Videos were processed to extract image frames to prepare a trainable<br/>dataset. For our experimentation, we have mainly used pretrained ResNet variants (ResNet18,<br/>ResNet34, ResNet50, ResNet101 & ResNet152) on ImageNet dataset and few other models like<br/>AlexNet, ConvNeXt_Tiny, DenseNet121, SqueezeNet and VGG11 for comparison purposes.<br/>Then modified the last classification layer of the network as per number of target classes and<br/>finetuned all weights of neural network on the subject dataset. We carried out various<br/>experiments on these neural networks and achieved quite encouraging results having accuracies<br/>ranging from 95% to 99%. For testing of videos on trained models, dynamic averaging time<br/>domain window was applied to diminish the jitters in the output results. This can be useful in<br/>many other applications as well including social media & advertisements analysis, classification<br/>of small videos, industrial and business automation etc. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Karam Dad Kallu |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/33976">http://10.250.8.41:8080/xmlui/handle/123456789/33976</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 01/11/2024 | 629.8 | SMME-TH-854 | Thesis |