M.Sc. Stefan Breuers|
Phone: +49 241 80 20768
Fax: +49 241 80 22731
TL;DR: Explorative paper. Learn a Triplet-ReID net, embed the full image. Keep embeddings of known tracks, correlate them with image embeddings and use that as measurement model in a Bayesian filtering tracker. MOT score is mediocre, but framework is theoretically pleasing.
With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-camera multi-target (MCMT) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter. This conveniently side-steps the need for data-association and opens up a direct path from full images to the core of the tracker. While the results are still sub-par, we believe that this new, tight integration opens many interesting research opportunities and leads the way towards full end-to-end tracking from raw pixels.
Tracking people is a key technology for robots and intelligent systems in human environments. Many person detectors, filtering methods and data association algorithms for people tracking have been proposed in the past 15+ years in both the robotics and computer vision communities, achieving decent tracking performances from static and mobile platforms in real-world scenarios. However, little effort has been made to compare these methods, analyze their performance using different sensory modalities and study their impact on different performance metrics. In this paper, we propose a fully integrated real-time multi-modal laser/RGB-D people tracking framework for moving platforms in environments like a busy airport terminal. We conduct experiments on two challenging new datasets collected from a first-person perspective, one of them containing very dense crowds of people with up to 30 individuals within close range at the same time. We consider four different, recently proposed tracking methods and study their impact on seven different performance metrics, in both single and multi-modal settings. We extensively discuss our findings, which indicate that more complex data association methods may not always be the better choice, and derive possible future research directions.
Many multi-object-tracking (MOT) techniques have been developed over the past years. The most successful ones are based on the classical tracking-by-detection approach. The different methods rely on different kinds of data association, use motion and appearance models, or add optimization terms for occlusion and exclusion. Still, errors occur for all those methods and a consistent evaluation has just started. In this paper we analyze three current state-of-the-art MOT trackers and show that there is still room for improvement. To that end, we train a classifier on the trackers' output bounding boxes in order to prune false positives. Furthermore, the different approaches have different strengths resulting in a reduced false negative rate when combined. We perform an extensive evaluation over ten common evaluation sequences and consistently show improved performances by exploiting the strengths and reducing the weaknesses of current methods.
We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real- time for mobile platforms. In this paper, we describe how the 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 empir- ical experiments to assess socio-psychological effects of normative robot behaviors.
We consider complementing Büchi automata by applying the Ramsey-based approach, which is the original approach already used by Büchi and later improved by Sistla et al. We present several heuristics to reduce the state space of the resulting complement automaton and provide experimental data that shows that our improved construction can compete (in terms of finished complementation tasks) also in practice with alternative constructions like rank-based complementation. Furthermore, we show how our techniques can be used to improve the Ramsey-based complementation such that the asymptotic upper bound for the resulting complement automaton is 2^O(n log n) instead of 2^O(n2).