NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201

Proximate Determinants of Women's Use of Birth Control Methods in Ota,Ogun State

Fertility regulation and pregnancy prevention are among the major health challenges of the 21st century in sub-Saharan Africa, especially in Nigeria. Contraception has been identified as an effective means of combating the problem of unwanted pregnancy and unsafe abortion and it is equally an effective means of family planning and fertility control and therefore very important in promoting maternal and child health. Women between ages 18 and 47 (n=143, mean=30.4 years) were sampled. A survey research questionnaire made up of four trajectories and consisting of sixteen (16) items was used in this study. The study made use of frequency counts, percentage, t-test analysis and regression analysis. The SPSS software was used to analyze the data. Results indicate a good knowledge of types of contraception with more than a third (83%) aware of condom as a contraceptive method. However, very few women were aware of modern contraceptive methods such as implants (9%) and spermicides (5%). The study indicates that knowledge of contraception (β = 2.244; t = 2.356; p < .05), employment status (β = 1.955; t = 2.257;p < .05) and age (β = 1.530; t = 2.203; p < .05) were good predictors of women’s contraceptive use. There was also a significant difference in women’s use of contraceptives based on contraceptive selfefficacy (t = 3.387, p < .05). Based on these findings, the study shows the need for strong advocacy,enlightenment and community mobilization for improved awareness and use of contraceptives in fertility control and preventing unwanted pregnancie

Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been popular as an efficient tool for encoding spatio-temporal features in sequences. In this work, we investigate the effect of mode variations on the encoded spatio-temporal features using LSTMs. We show that the LSTM retains information related to the mode variation in the sequence, which is irrelevant to the task at hand (e.g. classification facial expressions). Actually, the LSTM forget mechanism is not robust enough to mode variations and preserves information that could negatively affect the encoded spatio-temporal features. We propose the mode variational LSTM to encode spatio-temporal features robust to unseen modes of variation. The mode variational LSTM modifies the original LSTM structure by adding an additional cell state that focuses on encoding the mode variation in the input sequence. To efficiently regulate what features should be stored in the additional cell state, additional gating functionality is also introduced. The effectiveness of the proposed mode variational LSTM is verified using the facial expression recognition task. Comparative experiments on publicly available datasets verified that the proposed mode variational LSTM outperforms existing methods. Moreover, a new dynamic facial expression dataset with different modes of variation, including various modes like pose and illumination variations, was collected to comprehensively evaluate the proposed mode variational LSTM. Experimental results verified that the proposed mode variational LSTM encodes spatio-temporal features robust to unseen modes of variation.Comment: Accepted in AAAI-1

DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.Comment: CVPR 201

Towards an Efficient Context-Aware System: Problems and Suggestions to Reduce Energy Consumption in Mobile Devices

Looking for optimizing the battery consumption is an open issue, and we think it is feasible if we analyze the battery consumption behavior of a typical context-aware application to reduce context-aware operations at runtime. This analysis is based on different context sensors configurations. Actually existing context-aware approaches are mainly based on collecting and sending context data to external components, without taking into account how expensive are these operations in terms of energy consumption. As a first result of our work in progress, we are proposing a way for reducing the context data publishing. We have designed a testing battery consumption architecture supported by Nokia Energy Profiler tool to verify consumption in different scenarios

Living labs for in-situ open innovation: from idea to product validation and beyond

In this paper we present the Living Lab methodology as an overall framework for in-situ open innovation involving the end-user as equal participant in the innovation process. As a specific form of distributed innovation, relying on co-creation, we demonstrate the applicability of the Living Lab-approach for home ICT innovation by means of four innovation projects in different stages of maturity. We describe the used research methodologies and reflect on the role of the user

Microlensing towards M31 with MDM data

We report the final analysis of a search for microlensing events in the direction of the Andromeda galaxy, which aimed to probe the MACHO composition of the M31 halo using data collected during the 1998-99 observational campaign at the MDM observatory. In a previous paper, we discussed the results from a first set of observations. Here, we deal with the complete data set, and we take advantage of some INT observations in the 1999-2000 seasons. This merging of data sets taken by different instruments turns out to be very useful, the study of the longer baseline available allowing us to test the uniqueness characteristic of microlensing events. As a result, all the candidate microlensing events previously reported turn out to be variable stars. We further discuss a selection based on different criteria, aimed at the detection of short--duration events. We find three candidates whose positions are consistent with self--lensing events, although the available data do not allow us to conclude unambiguously that they are due to microlensing.Comment: Accepted for publication in Astronomy and Astrophysic