Active occlusion-handling for appearance-based object recognition models

Struwe M. Active occlusion-handling for appearance-based object recognition models. Bielefeld: Universität Bielefeld; 2017.Despite extensive research, visual detection of objects in natural scenes is still not robustly solved. The reason for this is the large variation in appearance in which objects or classes occur. A particularly challenging variation is occlusion, which is caused by the constellation of objects in a scene. Occlusion reduces the number of visible features of an object, but also causes accidental features. Current object representations yield acceptable results during a low to medium level of occlusion, but fail for stronger occlusions. This thesis addresses single image-based object recognition during occlusion and proposes different occlusion-handling strategies. Initially, it depicts a holistic discriminative car detection framework, which several chapters use as reference system. Motivated by a label analysis of hand-annotated video traffic scenes, it then presents a car detector, taking car-car constellations into account. The following chapter illustrates a modification of the reference system to cover with more general occlusion constellations. Inspired by the fact that parts-based detection approaches are more robust against occlusion, the next chapter discusses a parts-based car detector with active occlusion-handling at the detection step. At first, this exploits a strategy using the mask of the occluding object to re-weight the score of possible car hypotheses. This is followed by the presentation of an extended version, which especially targets strongly occluded cars. Due to the fact that hand-annotated video streams do not provide pixel-level information about the object instances, this thesis presents a rendered benchmark data set to resolve this issue. The pixel-level information permits intensive evaluation of occlusion-handling strategies. An eye-tracker study also uses this rendered data set to explore how humans cope with the absence of visual object features, and which information they use to deal with occlusion

A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second authorshi