Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

Browse

Author: search.filters.author.Dr. Usama Ijaz BajwaSubject: search.filters.subject.fa21

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Human Action Recognition in Low-Resolution Videos Based on Spatio-Temporal Features
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Samia Akram; FA21-RCS-015; LHR TP 8678; Dr. Usama Ijaz Bajwa
    In recent years, the field of computer vision and pattern recognition has devoted a lot of research focus to the study of human action. Most of the current action recognition research focuses on high-quality videos with clearly apparent actions. Most actions are of limited quality and take place at a distance, making it difficult to identify them. Therefore, the issue of low video quality is still under-researched and difficult to solve in practical implementation. The goal of this study is to establish a deep learning based framework for recognizing human actions in low-quality video by utilizing spatial-temporal features. This research focuses on the use of annotated dataset TinyVIRAT-v2 of action recognition videos recorded in low quality in order to examine the applicability of deep learning architecture for action recognition from videos of low perceptual quality. First, in the proposed framework datasets will be pre-processed and spatial-temporal features will be retrieved using a CNN-based feature extractor. The extracted features will then be fed into a 3D custom model for classification, which is based on resnet50 as backbone network and c3d with a SoftMax layer for multiclass prediction, allowing for its implementation as a real-world practical application. Upon classifying, every testing video is allocated a predicted category, and the cumulative outcomes are evaluated using performance measure F1-Score which is 0.68.
  • No Thumbnail Available
    Item
    Multiclass Weeds and Crops Classification and Segmentation using Deep Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Seemab Ayub; FA21-RCS-009; LHR TP 8675; Dr. Usama Ijaz Bajwa
    The world's population has increased exponentially with the increase in food demand, so it's essential to focus on factors that affect the quality and quantity of food. Some basic natural factors are climate, soil, pests, and weeds. Weeds are unwanted plants in yields that compete with the crops and consume a lot of nutrients that affect the quality and growth of crops. The separation of these toxic weeds is a challenging task because of their color and quantity. Pakistan is an agricultural country, and almost a big part of its economy depends on agriculture. The growth of this department does not match the population growth; the reason behind this is the presence of weeds. Automated technological interventions represent the optimal approach for weed removal. However, notable challenges persist within the technological landscape, including the absence of comprehensive datasets and the need for well-automated systems specifically tailored for crop weed classification and segmentation. Focusing on these issues, most of the researchers are working on different techniques, but the inadequate size of the dataset is still a problem for researchers, so this study performed to generate an efficient large dataset by merging different small datasets on the base to pick only those classes of weed and crops that belong to Pakistan; also an effective system using UNet variations (UNet, Attention UNet, and Attention Residual UNet(ARUNet)) and ResNet where ARUNet has performed very well for segmentation process and especially the Attention mechanism boosted the segmentation process, on the other hand ResNet has performed best for classification process on our dataset (24- classes and 120000 images). The accuracy of 99% achieved for classification process and 95% for segmentation.
  • No Thumbnail Available
    Item
    A Robust Attention-based 3D CNN Model for Fire and Smoke Detection from Videos
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Rimsha Shoukat; FA21-RCS-014; LHR TP 8677; Dr. Usama Ijaz Bajwa
    The unforeseen occurrence of a fire eruption has significant and adverse impacts on all aspects of human existence, including human life, land, industry, forests, and animal populations. The emergence of smoke before the apparent signs of fire makes smoke detection very advantageous in terms of promptly identifying fires. This early detection capability holds significant potential for preserving numerous lives and mitigating the occurrence of catastrophic events and associated losses. The problem of detecting smoke and fire using video-based methods presents significant challenges due to the considerable variability in their color, brightness, and shape. Most prior researchers conducted individual studies focused on the categorization of fire or smoke. Furthermore, video classification studies focused on a 2D approach, which was inadequate due to its inability to capture temporal information, so it was not able to learn the difference between frames. The convolutional neural network (CNN) has demonstrated exceptional performance in several domains, hence establishing itself as the leading approach. This study presents a robust classifier based on a three-dimensional convolutional neural network (3D-CNN) specifically developed to classify events into three distinct classes: fire, smoke, and neutral. The classifier is meant to effectively process video data. 3D models have the capability to acquire spatial and temporal characteristics from video sequences, rendering them appropriate for undertaking such tasks. The proposed 3D-CNN model had a notable test accuracy of 90% and showcased outstanding performance with an accuracy of 96% when subject to cross data [1] that have seven smoke videos. To enhance its capabilities, the model integrates the CBAM (Convolutional Block Attention Module) attention mechanism. The utilization of this approach allows the model to concentrate on significant regions of interest (ROI) throughout the entirety of the frame, hence enhancing its precision in categorizing complex attributes inside video frames. By integrating the attention mechanism, the model attained a remarkable gain of 6% in test accuracy with an accuracy rate of 96% and an F1-score of 98%.
  • No Thumbnail Available
    Item
    Classification of MGMT Promoter Methylation Status in Brain Tumor MR Images using Deep Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sohaib Iqbal; FA21-RCS-005; LHR TP 8470; Dr. Usama Ijaz Bajwa
    Glioblastoma is the most dangerous brain tumor type (worst prognosis), having the least survival rate of less than a year. O6-methylguanine-DNA methyltransferase (MGMT) promoter status “methylation” adds a favorable prognosis and is a reliable indication of cancer's chemosensitivity. Therefore, determining the MGMT status of a brain tumor patient is essential for treatment planning. Surgical tissue sample removal is required to conduct genetic testing for tumors. The genetic characterization of the tumors may then take a few weeks to establish after that. Non-invasive diagnosis using MR images plays a significant role in the early detection of the deadly disease by using Magnetic Resonance Imaging (MRI) to detect the tumorous regions using deep learning techniques. The main requirement for detecting MGMT status is obtaining the sub-regions of brain tumor from MRIs. Previous studies have worked on Brain tumor segmentation and MGMT classification separately, few of the studies have worked on segmentation and classification together but have used selective modalities for MGMT Prediction. Since each modality has its own significance and MGMT promoter status lies in the tumorous region, this study proposed a pipeline of segmentation and classification models using stacked multimodalities of MRI scans of the benchmark dataset BraTS2021. The pipeline is divided into two phases. The first phase uses a stack of multiple MRI modalities to segment the brain tumor into sub-region using a 3D Residual U-Net Architecture, and the second phase uses the segmentation model's output (stacked multimodalities tumor voxel) to determine the MGMT promoter status using a 3D ResNet 10 Classifier. With the help of pipeline, classification model received precise information about the tumorous regions which helped in efficient prediction of the MGMT status present in the brain tumor. The proposed pipeline has therapeutic value because it can assist radiologists in diagnosing brain tumors more efficiently and precisely by reducing the subjectivity and variability of human interpretation. The segmentation and classification pipeline for brain tumors x can potentially increase the consistency and objectivity of diagnoses. Furthermore, it can also assist radiologists in predicting the MGMT status without using surgical equipment and can help in treatment decision making. The segmentation phase of the proposed pipeline yielded promising results with average dice scores of 0.81, 0.84, and 0.80 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) on validation. The classification phase achieved ROC-AUC score of 0.66 on validation.