Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

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.Comment: First two authors have equal contribution. This is initial work into a new direction, not a benchmark-beating method. v2 only adds acknowledgements and fixes a typo in e-mai

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INCLUIDAS EN EL PEQUEÑO ÁLBUM FOTOGRÁFICO FAMILIAR DE LA COLECCIÓN LUIS SUÁREZ GALVÁNCopia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Dynamic remodeling of the plastid envelope membranes - a tool for chloroplast envelope in vivo localizations

Breuers FKH, Bräutigam A, Geimer S, et al. Dynamic remodeling of the plastid envelope membranes - a tool for chloroplast envelope in vivo localizations. Frontiers in Plant Science. 2012;3: 7.Two envelope membranes delimit plastids, the defining organelles of plant cells. The inner and outer envelope membranes are unique in their protein and lipid composition. Several studies have attempted to establish the proteome of these two membranes; however, differentiating between them is difficult due to their close proximity. Here, we describe a novel approach to distinguish the localization of proteins between the two membranes using a straightforward approach based on live cell imaging coupled with transient expression. We base our approach on analyses of the distribution of GFP-fusions, which were aimed to verify outer envelope membrane proteomics data. To distinguish between outer envelope and inner envelope protein localization, we used AtTOC64-GFP and AtTIC40-GFP, as respective controls. During our analyses, we observed membrane proliferations and loss of chloroplast shape in conditions of protein over-expression. The morphology of the proliferations varied in correlation with the suborganellar distribution of the over-expressed proteins. In particular, while layers of membranes built up in the inner envelope membrane, the outer envelope formed long extensions into the cytosol. Using electron microscopy, we showed that these extensions were stromules, a dynamic feature of plastids. Since the behavior of the membranes is different and is related to the protein localization, we propose that in vivo studies based on the analysis of morphological differences of the membranes can be used to distinguish between inner and outer envelope localizations of proteins. To demonstrate the applicability of this approach, we demonstrated the localization of AtLACS9 to the outer envelope membrane. We also discuss protein impact on membrane behavior and regulation of protein insertion into membranes, and provide new hypotheses on the formation of stromules

Multi-object tracking and person analysis from mobile robot platforms

Multi-object tracking is a broad and very active field of research in the area of computer vision. Finding the trajectories of multiple persons in a scene is an important key component in video analysis, surveillance, autonomous driving, as well as mobile robotics. The latter application has led to several international research projects, e.g., developing social service platforms, on whose results this thesis is based on. First, we study common approaches for image-based 2D multi-object tracking and analyze exemplary methods with regard to the errors they make. We propose a classifier that learns the situations where false positive tracks appear, based on bounding box context features. The individual characteristics allow for a combination of the trackers’ output and we show that this leads to an improved general result. This not only indicates that there is still potential to improve individual methods, but also that multi-object trackers have different strengths and we always need to take a full look on all the evaluation measures. When analyzing the results of those trackers it is therefore important to keep the application scenario in mind. As mentioned above, we have a look at robot platforms and examine how well recent multi-object tracking approaches perform in those 3D world situations. For this, we present a highly modular detection-tracking pipeline. We discuss important design choices, considering the chosen data association or the use of multi-modal detectors, where complex methods or more input, respectively, does not always lead to better tracking performance. We then extend the above pipeline to also integrate person analysis modules as another modular level. By using the unique trajectories, we can apply temporal filtering on the analysis output of each tracked person. On the example of head and body pose estimation, we show that this way, we get a smoothed, improved result of those attributes. Additionally, it is possible to run those filters with a certain stride, resulting in a huge performance boost when dealing with those expensive deep learning methods. Finally, we also explore a new multi-object tracking approach building on top of this successful deep learning framework. While existing methods often use deep appearance or motion models to help the data association step, we try to completely sidestep the dependency on a detector and therefore the need for data association. In order to do so, we make use of a strong re-identification model based on triplet loss inside an optimal Bayes filter, which forms the theoretical foundation of many tracking methods. By modeling track states as full probability maps, we can operate directly on the image input, taking a step towards an end-to-end image-to-track approach

Detection- Tracking for Efficient Person Analysis: The DetTA Pipeline

In the past decade many robots were deployed in the wild, and people detection and tracking is an important component of such deployments. On top of that, one often needs to run modules which analyze persons and extract higher level attributes such as age and gender, or dynamic information like gaze and pose. The latter ones are especially necessary for building a reactive, social robot-person interaction. In this paper, we combine those components in a fully modular detection-tracking-analysis pipeline, called DetTA. We investigate the benefits of such an integration on the example of head and skeleton pose, by using the consistent track ID for a temporal filtering of the analysis modules' observations, showing a slight improvement in a challenging real-world scenario. We also study the potential of a so-called “free-flight” mode, where the analysis of a person attribute only relies on the filter's predictions for certain frames. Here, our study shows that this boosts the runtime dramatically, while the prediction quality remains stable. This insight is especially important for reducing power consumption and sharing precious (GPU-)memory when running many analysis components on a mobile platform, especially so in the era of expensive deep learning methods