LiDAR-based 3D object detection and tracking for autonomous driving by Luo, Zhipeng

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Exploring different dehazing algorithms for object detection in foggy weather conditions for autonomous vehicles by Kim, Chae Yoon

“…With the advent of autonomous driving technology, the need for reliable object system in diverse conditions, including adverse weather, has become paramount. …”
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Panoptic image segmentation by Chua, Shahrin Zong Da

“…As a result, the application of Machine Learning to carry out everyday tasks has become increasingly commonplace, such as in autonomous driving. This project focuses on the Panoptic Image Segmentation task, an image segmentation method used for Computer Vision tasks, and aims to implement a real-time danger detection system. …”
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Sequence-to-sequence learning for motion prediction and generation by Wu, Shuang

“…The research field for computational understanding and modelling of human motion has garnered increasing importance in the last decade, with a plethora of applications in sports science, animation, robotics, surveillance and autonomous driving. In this thesis, we engage the sequence-to-sequence learning paradigm to study motion prediction and motion generation. …”
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Accurate and robust detection and recognition of texts in scene by Xue, Chuhui

“…Automated scene text detection and recognition have attracted increasing interest in computer vision and deep learning communities due to its wide range of applications in neural machine translation, autonomous driving, etc. As compared with preliminary research that focuses on the design of hand-crafted features, modern deep-learning-based techniques have achieved significant improvements on scene text detection and recognition tasks. …”
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Object detection with deep neural networks under constrained scenarios by Zhang, Gongjie

“…Object detection forms the basis for many other computer vision tasks and has extensive use cases, such as autonomous driving, surveillance, robotic vision, etc. In the past ten years, object detection has made unprecedented progress with the development of deep neural networks. …”
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