Cross-View Action Recognition from Temporal Self-Similarities

This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric cues captured by self-similarities and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only

Efficient Estimation of Human Upper Body Pose in Static Depth Images

Automatic estimation of human pose has long been a goal of computer vision, to which a solution would have a wide range of applications. In this paper we formulate the pose estimation task within a regression and Hough voting framework to predict 2D joint locations from depth data captured by a consumer depth camera. In our approach the offset from each pixel to the location of each joint is predicted directly using random regression forests. The predictions are accumulated in Hough images which are treated as likelihood distributions where maxima correspond to joint location hypotheses. Our approach is evaluated on a publicly available dataset with good results. © Springer-Verlag Berlin Heidelberg 2013

Benchmark RGB-D Gait Datasets: A Systematic Review

Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms. (c) Springer Nature Switzerland AG 2019

Monocular Expressive Body Regression through Body-Driven Attention

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.d

Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria

Abstract Background Estuaries are among the most productive habitats on the planet. Bacteria in estuary sediments control the turnover of organic carbon and the cycling of nitrogen and sulfur. These communities are complex and primarily made up of uncultured lineages, thus little is known about how ecological and metabolic processes are partitioned in sediments. Results De novo assembly and binning resulted in the reconstruction of 82 bacterial genomes from different redox regimes of estuary sediments. These genomes belong to 23 bacterial groups, including uncultured candidate phyla (for example, KSB1, TA06, and KD3-62) and three newly described phyla (White Oak River (WOR)-1, WOR-2, and WOR-3). The uncultured phyla are generally most abundant in the sulfate-methane transition (SMTZ) and methane-rich zones, and genomic data predict that they mediate essential biogeochemical processes of the estuarine environment, including organic carbon degradation and fermentation. Among the most abundant organisms in the sulfate-rich layer are novel Gammaproteobacteria that have genes for the oxidation of sulfur and the reduction of nitrate and nitrite. Interestingly, the terminal steps of denitrification (NO3 to N2O and then N2O to N2) are present in distinct bacterial populations. Conclusions This dataset extends our knowledge of the metabolic potential of several uncultured phyla. Within the sediments, there is redundancy in the genomic potential in different lineages, often distinct phyla, for essential biogeochemical processes. We were able to chart the flow of carbon and nutrients through the multiple geochemical layers of bacterial processing and reveal potential ecological interactions within the communities.http://deepblue.lib.umich.edu/bitstream/2027.42/111044/1/40168_2015_Article_77.pd