Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Computationally efficient deformable 3D object tracking with a monocular RGB camera

182 p.Monocular RGB cameras are present in most scopes and devices, including embedded environments like robots, cars and home automation. Most of these environments have in common a significant presence of human operators with whom the system has to interact. This context provides the motivation to use the captured monocular images to improve the understanding of the operator and the surrounding scene for more accurate results and applications.However, monocular images do not have depth information, which is a crucial element in understanding the 3D scene correctly. Estimating the three-dimensional information of an object in the scene using a single two-dimensional image is already a challenge. The challenge grows if the object is deformable (e.g., a human body or a human face) and there is a need to track its movements and interactions in the scene.Several methods attempt to solve this task, including modern regression methods based on Deep NeuralNetworks. However, despite the great results, most are computationally demanding and therefore unsuitable for several environments. Computational efficiency is a critical feature for computationally constrained setups like embedded or onboard systems present in robotics and automotive applications, among others.This study proposes computationally efficient methodologies to reconstruct and track three-dimensional deformable objects, such as human faces and human bodies, using a single monocular RGB camera. To model the deformability of faces and bodies, it considers two types of deformations: non-rigid deformations for face tracking, and rigid multi-body deformations for body pose tracking. Furthermore, it studies their performance on computationally restricted devices like smartphones and onboard systems used in the automotive industry. The information extracted from such devices gives valuable insight into human behaviour a crucial element in improving human-machine interaction.We tested the proposed approaches in different challenging application fields like onboard driver monitoring systems, human behaviour analysis from monocular videos, and human face tracking on embedded devices

Efficient multi-task based facial landmark and gesture detection in monocular images

[EN] The communication between persons includes several channels to exchange information between individuals. The non-verbal communication contains valuable information about the context of the conversation and it is a key element to understand the entire interaction. The facial expressions are a representative example of this kind of non-verbal communication and a valuable element to improve human-machine interaction interfaces. Using images captured by a monocular camera, automatic facial analysis systems can extract facial expressions to improve human-machine interactions. However, there are several technical factors to consider, including possible computational limitations (e.g. autonomous robots), or data throughput (e.g. centralized computation server). Considering the possible limitations, this work presents an efficient method to detect a set of 68 facial feature points and a set of key facial gestures at the same time. The output of this method includes valuable information to understand the context of communication and improve the response of automatic human-machine interaction systems

On-demand serverless video surveillance with optimal deployment of deep neural networks

[EN] We present an approach to optimally deploy Deep Neural Networks (DNNs) in serverless cloud architectures. A serverless architecture allows running code in response to events, automatically managing the required computing resources. However, these resources have limitations in terms of execution environment (CPU only), cold starts, space, scalability, etc. These limitations hinder the deployment of DNNs, especially considering that fees are charged according to the employed resources and the computation time. Our deployment approach is comprised of multiple decoupled software layers that allow effectively managing multiple processes, such as business logic, data access, and computer vision algorithms that leverage DNN optimization techniques. Experimental results in AWS Lambda reveal its potential to build cost-effective ondemand serverless video surveillance systems.This work has been partially supported by the program ELKARTEK 2019 of the Basque Government under project AUTOLIB

Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.This work was supported by the SHAPES project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 857159, and in part by the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project ÉGIDA—RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD under Grant CER20191012