MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is our new differentiable parametric decoder that encapsulates image formation analytically based on a generative model. Our decoder takes as input a code vector with exactly defined semantic meaning that encodes detailed face pose, shape, expression, skin reflectance and scene illumination. Due to this new way of combining CNN-based with model-based face reconstruction, the CNN-based encoder learns to extract semantically meaningful parameters from a single monocular input image. For the first time, a CNN encoder and an expert-designed generative model can be trained end-to-end in an unsupervised manner, which renders training on very large (unlabeled) real world data feasible. The obtained reconstructions compare favorably to current state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13 page

Pelican Crossing System for Control a Green Man Light with Predicted Age

Traffic lights are generally used to regulate the control flow of traffic at an intersection from all directions, including a pelican crossing system with traffic signals for pedestrians. There are two facilities for walker crossing, namely using a pedestrian bridge and a zebra cross. In general, the traffic signals of the pelican crossing system are a fixed time, whereas other pedestrians need "green man" traffic lights with duration time arrangement. Our research proposes a prototype intelligent pelican crossing system for somebody who crosses the road at zebra crossings, but the risk of falling while crossing is not expected, especially in the elderly age group or pedestrians who are pregnant or carrying children. On the other hand, the problem is that the average step length or stride length (distance in centimeter), cadence or walking rate (in steps per minute), and the possibility of accidents are very high for pedestrians to make sure do crossing during the lights “green man”. The new idea of our research aims to set the adaptive time arrangement on the pelican crossing intelligent system of the traffic lights “green man” based on the age of the pedestrians with artificial intelligence using two combined methods of the FaceNet and AgeNet. The resulting measure can predict the age of pedestrians of the training dataset of 66.67% and testing prototype in real-time with participants on the pelican crossing system of 73% (single face) and 76% (multi faces)

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes