Video Object Segmentation And Tracking

Object detection and segmentation are some of the key components of Computer Vision, which have wide ranging real world applications. The current state of the art techniques in computer vision are based on Deep Neural Networks and one of the key challenges is using the state of the art techniques in these fields on novel images, and videos in different environments, and classes. These methods require expensive manual annotations and transfer learning to make them work on domains different from their training data sets. In this thesis, we explore both domain adaptation, and deep learning techniques that don't necessarily rely on the idea of a class, to help with the annotation of private videos. We implemented the initial idea of domain adaptation for directly annotating objects and followed with using video object segmentation (VOS) tracking methods for propagating annotations. Their application to a novel video acquired in the GURU lab is explored as well as ways to improve their performance.

Unmanned Aerial Vehicles (UAVs) equipped with video cameras are a flexible support to ensure civil and military safety and security. In this thesis, a video processing chain is presented for moving object detection in aerial video surveillance. A Track-Before-Detect (TBD) algorithm is applied to detect motion that is independent of the camera motion. Novel robust and fast object detection and segmentation approaches improve the baseline TBD and outperform current state-of-the-art methods.

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.