Abstract:
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.
