Lu Zhang

TL;DR: How the SPENCER project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors is described.

TL;DR: The experimental evaluation of the structure-preserving object tracker (SPOT) reveals significant performance improvements in multi-object tracking and shows that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object.

TL;DR: The experimental evaluation of the structure-preserving object tracker (SPOT) reveals substantial performance improvements in multi-object tracking and shows that SPOT can be used to adapt generic, model-based object detectors during tracking to tailor them towards a specific instance of that object.

TL;DR: This paper presents the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images and generates a richer model of the social interactions in a scene but also significantly improve F-formation detection.

TL;DR: The paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that -- after inspecting a few features -- are unlikely to contain the target object.