In this project, we will develop a low-latency, robust to occlusion, object tracker. Three main paradigms exist in the literature to perform object tracking: Tracking-by-detection, Tracking-by-regression, and Tracking-by-attention. We will start with a deep literature review to evaluate the current solutions to our end goal of being fast and robust to occlusion. Starting from the conclusions of this study, we will design a novel tracker that can achieve our goal. In addition to RBG images, we will investigate other sensor modalities such as inertial measurement units and event cameras.
This project is done in collaboration with Meta.
In this project, we will develop a low-latency, robust to occlusion, object tracker. Three main paradigms exist in the literature to perform object tracking: Tracking-by-detection, Tracking-by-regression, and Tracking-by-attention. We will start with a deep literature review to evaluate the current solutions to our end goal of being fast and robust to occlusion. Starting from the conclusions of this study, we will design a novel tracker that can achieve our goal. In addition to RBG images, we will investigate other sensor modalities such as inertial measurement units and event cameras. This project is done in collaboration with Meta.
Develop a low-latency object tracker that is robust to occlusions.
We look for students with strong computer vision background and familiar with common software tools used in Deep Learning (for example, PyTorch or TensorFlow).
Develop a low-latency object tracker that is robust to occlusions. We look for students with strong computer vision background and familiar with common software tools used in Deep Learning (for example, PyTorch or TensorFlow).