Computer Vision Algorithms for Mobile Camera Applications

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices

Supporting independent living for older adults; employing a visual based fall detection through analysing the motion and shape of the human body

Falls are one of the greatest risks for older adults living alone at home. This paper presents a novel visual-based fall detection approach to support independent living for older adults through analysing the motion and shape of the human body. The proposed approach employs a new set of features to detect a fall. Motion information of a segmented silhouette when extracted can provide a useful cue for classifying different behaviours, while variation in shape and the projection histogram can be used to describe human body postures and subsequent fall events. The proposed approach presented here extracts motion information using best-fit approximated ellipse and bounding box around the human body, produces projection histograms and determines the head position over time, to generate 10 features to identify falls. These features are fed into a multilayer perceptron neural network for fall classification. Experimental results show the reliability of the proposed approach with a high fall detection rate of 99.60% and a low false alarm rate of 2.62% when tested with the UR Fall Detection dataset. Comparisons with state of the art fall detection techniques show the robustness of the proposed approach

Wearable IMU for Shoulder Injury Prevention in Overhead Sports

Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator

New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

© 2015 by the authors; licensee MDPI, Basel, Switzerland. In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity

IMUs: validation, gait analysis and system’s implementation

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a prevalent problem in actual society. The number of falls has been increasing greatly in the last fifteen years. Some falls result in injuries and the cost associated with their treatment is high. However, this is a complex problem that requires several steps in order to be tackled. Namely, it is crucial to develop strategies that recognize the mode of locomotion, indicating the state of the subject in various situations, namely normal gait, step before fall (pre-fall) and fall situation. Thus, this thesis aims to develop a strategy capable of identifying these situations based on a wearable system that collects information and analyses the human gait. The strategy consists, essentially, in the construction and use of Associative Skill Memories (ASMs) as tools for recognizing the locomotion modes. Consequently, at an early stage, the capabilities of the ASMs for the different modes of locomotion were studied. Then, a classifier was developed based on a set of ASMs. Posteriorly, a neural network classifier based on deep learning was used to classify, in a similar way, the same modes of locomotion. Deep learning is a technique actually widely used in data classification. These classifiers were implemented and compared, providing for a tool with a good accuracy in recognizing the modes of locomotion. In order to implement this strategy, it was previously necessary to carry out extremely important support work. An inertial measurement units’ (IMUs) system was chosen due to its extreme potential to monitor outpatient activities in the home environment. This system, which combines inertial and magnetic sensors and is able to perform the monitoring of gait parameters in real time, was validated and calibrated. Posteriorly, this system was used to collect data from healthy subjects that mimicked Fs. Results have shown that the accuracy of the classifiers was quite acceptable, and the neural networks based classifier presented the best results with 92.71% of accuracy. As future work, it is proposed to apply these strategies in real time in order to avoid the occurrence of falls.As quedas são um problema predominante na sociedade atual. O número de quedas tem aumentado bastante nos últimos quinze anos. Algumas quedas resultam em lesões e o custo associado ao seu tratamento é alto. No entanto, trata-se de um problema complexo que requer várias etapas a serem abordadas. Ou seja, é crucial desenvolver estratégias que reconheçam o modo de locomoção, indicando o estado do sujeito em várias situações, nomeadamente, marcha normal, passo antes da queda (pré-queda) e situação de queda. Assim, esta tese tem como objetivo desenvolver uma estratégia capaz de identificar essas situações com base num sistema wearable que colete informações e analise a marcha humana. A estratégia consiste, essencialmente, na construção e utilização de Associative Skill Memories (ASMs) como ferramenta para reconhecimento dos modos de locomoção. Consequentemente, numa fase inicial, foram estudadas as capacidades das ASMs para os diferentes modos de locomoção. Depois, foi desenvolvido um classificador baseado em ASMs. Posteriormente, um classificador de redes neuronais baseado em deep learning foi utilizado para classificar, de forma semelhante, os mesmos modos de locomoção. Deep learning é uma técnica bastante utilizada em classificação de dados. Estes classificadores foram implementados e comparados, fornecendo a uma ferramenta com uma boa precisão no reconhecimento dos modos de locomoção. Para implementar esta estratégia, era necessário realizar previamente um trabalho de suporte extremamente importante. Um sistema de unidades de medição inercial (IMUs), foi escolhido devido ao seu potencial extremo para monitorizar as atividades ambulatórias no ambiente domiciliar. Este sistema que combina sensores inerciais e magnéticos e é capaz de efetuar a monitorização de parâmetros da marcha em tempo real, foi validado e calibrado. Posteriormente, este Sistema foi usado para adquirir dados da marcha de indivíduos saudáveis que imitiram quedas. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e o classificador baseado em redes neuronais apresentou os melhores resultados com 92.71% de precisão. Como trabalho futuro, propõe-se a aplicação destas estratégias em tempo real de forma a evitar a ocorrência de quedas

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing