Photoplethysmography and its application in clinical physiological measurement

Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.

Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Intelligent systems and communication technologies have experienced huge advances in the last few years. AAL can benefit from mixing both research fields. This article presents an intelligent communication architecture for AAL. It uses artificial intelligence to process the information gathered from several types of communication (e.g., wireless sensor networks, wireless ad hoc networks, wireless mesh networks) over any type of communication technologies (e.g., deviceto- device, machine-to-machine, sensor-actuator), know what is happening in the network, and detect if elderly people need assistance. The article shows the main intelligent algorithms included in the AAL system and the developed software application. Several real measurements validate the operation of our proposal.

A smart communication architecture for ambient assisted living

The rapid aging of the population occurred in recent years has encouraged the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this paper, an Ambient Assisted Living (AAL) system has been designed in order to create better living conditions for older people. In this way, people can live independently longer in their own house with an improved quality of life. The proposed system includes several features. On the one hand, it is able to continuously monitor the health status of the elderly through data coming from heterogeneous sources (i.e., environmental sensors and medical devices). On the other hand, it is able to guarantee outdoor and indoor localization aimed to know the real-time position of the elderly both inside and outside their home. A remote reasoning system processes all collected data with the aim of generating appropriate events and alerts. The architecture was validated from a functional point of view through a proof-of-concept.

An IoT-aware AAL system for elderly people

In this paper an automated video based real-time surveillance system is presented. This system is based on an omnidirectional camera and a multiple object tracking technique for applications in the field of AAL (Ambient Assisted Living). This system is able to monitor a complete room with a single camera and, in addition, to track the people entering and leaving this room. The software was implemented for an embedded platform which acts as a smart sensor.

Automated real-time surveillance for ambient assisted living using an omnidirectional camera

In recent years, video surveillance combined with computer vision algorithms like object detection, tracking or automated behaviour analysis has become an important research topic. However, most of these systems are depending on either fixed or remotely controlled narrow angle cameras. When using the former, the area of coverage is extremely limited, while utilizing the latter leads to high failure rates and troubles in camera calibration. In this paper, a method of extracting multiple perspective views from a single omnidirectional image for realtime environments is proposed. An example application of a ceiling-mounted camera setup is used to show the functional principle. Furthermore a performance improvement strategy is both presented and evaluated.

A fast approach for omnidirectional surveillance with multiple virtual perspective views

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://mytuc.org/byfs

/pdf/omniflow-human-omnidirectional-optical-flow-4trry440aq.pdf

OmniFlow: Human Omnidirectional Optical Flow

We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: non-maximum suppression, soft non-maximum suppression and soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.

Improved Person Detection on Omnidirectional Images with Non-maxima Suppression

An emerging topic in the field of elderly care is the determination and tracking of vital parameters, such as the heart rate. This parameter provides important information about a personâs current health status. Within the last years, various research focussed on this topic. The recognition of vital parameters is increasingly relevant for our ageing society. This paper presents a method to remotely determine the human heart rate with a camera. At this point, we suggest to use independent component analysis (ICA) and adaptive filtering for a robust detection. In our processing chain, we used different image processing techniques, e. g. face detection, and signal processing techniques, e. g. FFT and bandpass filtering, in this study. An evaluation with several probands, illuminations, frame rates and different heart rate levels showed that we could achieve a mean error of 4.36 BPM, which corresponds to CAND of 94.45 %, and a speed of 35 fps.

André Apitzsch

Papers

Automated real-time surveillance for ambient assisted living using an omnidirectional camera

A fast approach for omnidirectional surveillance with multiple virtual perspective views

OmniFlow: Human Omnidirectional Optical Flow

Improved Person Detection on Omnidirectional Images with Non-maxima Suppression

Remote Heart Rate Determination in RGB Data