@phdthesis{Omranphd2021,
TITLE = {From Pixels to People},
AUTHOR = {Omran, Mohamed},
LANGUAGE = {eng},
URL = {nbn:de:bsz:291--ds-366053},
DOI = {10.22028/D291-36605},
SCHOOL = {Universit{\"a}t des Saarlandes},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2021},
DATE = {2021},
ABSTRACT = {Abstract<br>Humans are at the centre of a significant amount of research in computer vision.<br>Endowing machines with the ability to perceive people from visual data is an immense<br>scientific challenge with a high degree of direct practical relevance. Success in automatic<br>perception can be measured at different levels of abstraction, and this will depend on<br>which intelligent behaviour we are trying to replicate: the ability to localise persons in<br>an image or in the environment, understanding how persons are moving at the skeleton<br>and at the surface level, interpreting their interactions with the environment including<br>with other people, and perhaps even anticipating future actions. In this thesis we tackle<br>different sub-problems of the broad research area referred to as "looking at people",<br>aiming to perceive humans in images at different levels of granularity.<br>We start with bounding box-level pedestrian detection: We present a retrospective<br>analysis of methods published in the decade preceding our work, identifying various<br>strands of research that have advanced the state of the art. With quantitative exper-<br>iments, we demonstrate the critical role of developing better feature representations<br>and having the right training distribution. We then contribute two methods based<br>on the insights derived from our analysis: one that combines the strongest aspects of<br>past detectors and another that focuses purely on learning representations. The latter<br>method outperforms more complicated approaches, especially those based on hand-<br>crafted features. We conclude our work on pedestrian detection with a forward-looking<br>analysis that maps out potential avenues for future research.<br>We then turn to pixel-level methods: Perceiving humans requires us to both separate<br>them precisely from the background and identify their surroundings. To this end, we<br>introduce Cityscapes, a large-scale dataset for street scene understanding. This has since<br>established itself as a go-to benchmark for segmentation and detection. We additionally<br>develop methods that relax the requirement for expensive pixel-level annotations, focusing<br>on the task of boundary detection, i.e. identifying the outlines of relevant objects and<br>surfaces. Next, we make the jump from pixels to 3D surfaces, from localising and<br>labelling to fine-grained spatial understanding. We contribute a method for recovering<br>3D human shape and pose, which marries the advantages of learning-based and model-<br>based approaches.<br>We conclude the thesis with a detailed discussion of benchmarking practices in<br>computer vision. Among other things, we argue that the design of future datasets<br>should be driven by the general goal of combinatorial robustness besides task-specific<br>considerations.},
}