Browsing by Author "Prof. Dr. Zulfiqar Habib"

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A Person Reidentification Framework for Multi- Camera Tracking by Using Tracking-by-Detection Paradigm
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Mian Muhammad Abu Bakar; CIIT/FA22-RCS-008/LHR; Prof. Dr. Zulfiqar Habib; LHR TP 9697
The research is based on offline video-based reidentification of individuals by tracking them across multiple non-overlapping cameras, aiming to achieve accurate, efficient, and effective video-based person reidentification by extracting discriminative human attributes and robustly utilizing spatio-temporal complementary features in the tracking process. Person reidentification (Re-ID) aims to identify individuals with the same identities across multiple cameras at particular or different instances of time from images or video sequences. It involves applying deep learning algorithms that help to track people’s positions, motion, and direction. Over the past few years, deep learning- based methods have achieved significant success in this domain. These methods can be categorized as deep metric learning, micro-level feature learning, generative adversarial learning, domain adaptation and transfer learning, sequence feature learning, transformer-based methods, and clustering-based methods. Specifically, sequence feature learning, which focuses on fusion of spatio-temporal information from multiple dimensions, and transfer-learning-based methods, which focus on using pre-trained models trained in one domain and fine-tuning these models by providing samples of the target domain. These methods have achieved better experimental performances compared to the other deep learning-based methods, with significant improvements in accuracy. In the context of person reidentification, persistent challenges such as heavy occlusion and similar appearances, which lead to ID switches, that affect the person reidentification and tracking process, still remain an active area of research. To address these issues, a Single-Camera People Tracking mechanism named Redundancy- Elimination Clustering (REC) is introduced. ID switches mostly occur due to background clutter, heavy occlusion, similar appearances of individuals, or when two or more individuals cross each other in a camera view. These ID switches affect the process of reidentification and tracking. As a person cannot be tracked in different locations at the same time, it becomes clear that the possibility of the occurrence of the ID switches is due to the presence of multiple individuals in the same frame. The frames in which there are multiple individuals are termed here as the Redundant Frames, and we employ Redundancy-Elimination Clustering (REC) to resolve this issue through Hierarchical Clustering. To handle ID switches across multiple cameras, we propose Feature Supervised Clustering (FSC), which utilizes the Hungarian Algorithm and assigns unique global IDs to individuals, enabling robust cross-camera association. To correct the incorrect global IDs assigned to the individuals in the previous Feature Supervised Clustering (FSC) stage, either due to very similar appearances or due to extreme occlusion, we introduce Cross-Camera ID Refinement (CCIR). This method effectively assigns unique global IDs to individuals. In the end, some post-processing, like linear interpolation and handling of edge cases, is also performed. The final proposed method achieved IDF1 (95.67), IDP (96.26), IDR (95.13), Precision (96.26) and Recall (95.86), compared to the base methodology which achieved IDF1 (93.72), IDP (91.8), IDR (95.75), Precision (91.83) and Recall (95.64), which shows the robustness of the proposed method.
Anomaly Prediction and Analysis Using Deep Learning-Based Techniques
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Usman Hanif; CIIT/FA22-RCS-018/LHR; Prof. Dr. Zulfiqar Habib; LHR TP 9699
In today's world, where public safety is a growing concern, intelligent surveillance systems play a crucial role in identifying abnormal behavior in public places. To build an effective surveillance system, early prediction of abnormal actions is crucial as it can help avoid potentially dangerous situations before they occur. This thesis focused on the development of a deep learning-based anomaly prediction model that can predict future abnormal actions based on partially observed information of actions from surveillance videos. These videos are often captured under real-world environmental conditions such as poor lighting, occlusions, dynamic backgrounds, visual noise, etc., and only partial information of an action is considered for prediction, which makes the task of forecasting more challenging. Although previous studies have shown progress in anomaly recognition, which focuses on recognizing abnormal action only after it occurs completely, rather than predicting future action. In addition, many existing methods rely on spatial information and the generation of image-based next anomaly frames, usually ignoring the importance of temporal information. This limits its effectiveness in real-world scenarios in which understanding the action sequence and progress of actions over time is crucial. To address these limitations, this research proposed a novel deep learning-based hybrid approach that combines spatial and temporal information. The proposed model uses a pre-trained ResNet50 to extract spatial features and BiLSTM with an attention mechanism to learn temporal dependencies throughout the video. The final classification is performed using a fully connected layer with softmax activation function. Extensive experiments are carried out on the real-world challenging UCF-Crime dataset, where the model is evaluated at various observation ratios, such as 20%, 40%, 60%, and 80% of the video, to simulate early prediction scenarios. The results show that the proposed model achieved 81.4% accuracy, significantly outperforming baseline predictive models, even when only a limited portion of the action was observed, confirming their effectiveness in the early prediction of anomalies. In addition, the model achieved 90.3% accuracy when tested on complete video (100% video observation ratio), acting as a conventional anomalous action recognition. Although the anomaly prediction task achieved lower accuracy than anomaly recognition due to limited information available at early stages, the proposed model can make accurate future predictions much earlier. This makes it suitable for time-critical surveillance scenarios, where early predictions of crimes are more valuable. This study contributes to the advancement of proactive surveillance systems, allowing timely responses to anomalous events before they occur
Smart Learning Advance Tools for Education (SLATE) in Math for Grades 3-4 for Windows
(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Muhammad Qasim; SP16-BCS-102; Prof. Dr. Zulfiqar Habib; LHR TP 6286
Smart Learning Advanced Tools for Education (SLATE) is the part of a mega project on self-learning, evaluation, reporting to teachers and parents. We are developing SLATE in Math for grades 3 and 4 for windows platform. This application is based on creative learning methodologies with the help of animated video, practice forms and evaluation exercises. The concepts of the Punjab Text Book Board and Oxford are kept in mind while developing the project. Learning for students is made easy, attractive and exciting with the help of animation videos and practice exercises. Both practice and evaluation are done on randomly generated questions. Further, the design is developed in such a way that children will learn it in the first blink.