Browsing by Author "LHR TP 5972"

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    Anchor Free Motorbike Detection in Surveillance Videos of Dense Traffic
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Mahwish Irshad; FA16-RCS-029; LHR TP 5972; Dr. Zulfiqar Habib
    Object detection and tracking have become the most significant and challenging task within the field of Computer Vision that attempts to detect, recognize, and track objects over any sequence of frames (images) called video. Its purpose is to locate objects motion in a video file or surveillance camera. Object detection is the procedure of locating one or multiple objects by utilizing a single camera, multiple cameras, or a provided video file. Urban area expansions have increased the demand for proper traffic surveillance. Traffic monitoring is important for detecting road accidents, collecting evidence for the investigation, tracking criminals, and traffic violators. To meet these requirements, intelligent systems can be developed to extract and analyze traffic information. This research work is concerned with the detection of motorbikes in videos. Detection of motorbikes object is very helpful for organizations and investigators, which are mostly concerned in dealing with potential violations of human rights on roads. For the detection of motorbike objects a state-of-the-art dataset called Velastin7500 is used. The thesis focused on the detection of motorbikes from videos by using two pre-trained, RetinaNet, and YOLOv3 architectures on the Velastin7500 dataset to explore the importance of object detection from images, captured by a drone camera. This research work has also inspected the domain shift issue of learning of features from images to detect objects and by pertaining this information to real-world imagery in the context of detection of motorbike. The proposed work also explored serious issues in the existing algorithm based on anchor free technique for some practical applications. The results produced for the Velastin7500 dataset by implementing above mentioned two architectures of RetinaNet and YOLOv3 have mean average precision (mAP) value of 18.25% and 76.5%, respectively. This research will also be helpful to understand why the anchor free approach did not work well with partially visible objects.