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

Review on recent Computer Vision Methods for Human Action Recognition

The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition./nExisting modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper.La temo de homa agado-rekono estas konsiderata grava celo en la regado de komputila vizio ekde la komenco de ?ia disvolvi?o kaj atingis novajn nivelojn. ?i anka? estas pensata kiel simpla procedo. Problemoj ekestas en rapidaj kaj progresintaj scenoj, kaj la nombra analizo de artefarita inteligenteco (AI) per agado-anta?diro mistraktado pliigis la atenton de esploristoj por studi. Havante decajn metodikajn kaj enhavajn rilatajn varia?ojn, pluraj datenserioj estis kreitaj por trakti la taksadon de ?i tiuj manieroj. Homaj agadoj ludas gravan rolon sed kun malfacila karakteriza?o en diversaj kampoj. Multaj aplikoj ekzistas en ?i tiu kampo, kiel inteligenta hejmo, helpema AI, HCI (Homa-Komputila Interagado), progresoj en protekto en aplikoj kiel transportado, edukado, sekureco kaj administrado de medikamentoj, inkluzive faladon a? helpon al maljunuloj pri kuracado de drogoj. La pozitiva efiko de profundaj lernaj teknikoj sur multaj vidaj aplikoj kondukas al disfaldi ?i tiujn manierojn en video-prilaborado. Analizo de homaj kondutagadoj implikas gravajn defiojn kiam homa ?eesto temas. Unu individuo povas esti reprezentita en multoblaj videosekvencoj tra skeleto, movi?o kaj / a? abstraktaj karakteriza?oj. ?i tiu verko celas trakti homan ?eeston kombinante multajn eblojn kaj uzante novan RNN-strukturon por agadoj. La papero temigas lastatempajn progresojn en ma?inlernado-helpata agado.Ekzistantaj modernaj teknikoj por la rekono de agoj kaj prognozo simile ?ar la estonta amplekso por la analizo estas menciita precizeco ene de la recenzo-papero

An Outlook into the Future of Egocentric Vision

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.Comment: We invite comments, suggestions and corrections here: https://openreview.net/forum?id=V3974SUk1

Understanding egocentric human actions with temporal decision forests

Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions. The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces

Egocentric vision-based passive dietary intake monitoring

Egocentric (first-person) perception captures and reveals how people perceive their surroundings. This unique perceptual view enables passive and objective monitoring of human-centric activities and behaviours. In capturing egocentric visual data, wearable cameras are used. Recent advances in wearable technologies have enabled wearable cameras to be lightweight, accurate, and with long battery life, making long-term passive monitoring a promising solution for healthcare and human behaviour understanding. In addition, recent progress in deep learning has provided an opportunity to accelerate the development of passive methods to enable pervasive and accurate monitoring, as well as comprehensive modelling of human-centric behaviours. This thesis investigates and proposes innovative egocentric technologies for passive dietary intake monitoring and human behaviour analysis. Compared to conventional dietary assessment methods in nutritional epidemiology, such as 24-hour dietary recall (24HR) and food frequency questionnaires (FFQs), which heavily rely on subjects’ memory to recall the dietary intake, and trained dietitians to collect, interpret, and analyse the dietary data, passive dietary intake monitoring can ease such burden and provide more accurate and objective assessment of dietary intake. Egocentric vision-based passive monitoring uses wearable cameras to continuously record human-centric activities with a close-up view. This passive way of monitoring does not require active participation from the subject, and records rich spatiotemporal details for fine-grained analysis. Based on egocentric vision and passive dietary intake monitoring, this thesis proposes: 1) a novel network structure called PAR-Net to achieve accurate food recognition by mining discriminative food regions. PAR-Net has been evaluated with food intake images captured by wearable cameras as well as those non-egocentric food images to validate its effectiveness for food recognition; 2) a deep learning-based solution for recognising consumed food items as well as counting the number of bites taken by the subjects from egocentric videos in an end-to-end manner; 3) in light of privacy concerns in egocentric data, this thesis also proposes a privacy-preserved solution for passive dietary intake monitoring, which uses image captioning techniques to summarise the image content and subsequently combines image captioning with 3D container reconstruction to report the actual food volume consumed. Furthermore, a novel framework that integrates food recognition, hand tracking and face recognition has also been developed to tackle the challenge of assessing individual dietary intake in food sharing scenarios with the use of a panoramic camera. Extensive experiments have been conducted. Tested with both laboratory (captured in London) and field study data (captured in Africa), the above proposed solutions have proven the feasibility and accuracy of using the egocentric camera technologies with deep learning methods for individual dietary assessment and human behaviour analysis.Open Acces

Building an Understanding of Human Activities in First Person Video using Fuzzy Inference

Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by interaction with objects. We leverage these object-centric activity definitions to develop a set of rules for a Fuzzy Inference System (FIS) that uses video features and the identification of objects to identify and classify activities. Further, we demonstrate that the use of FIS enhances the reliability of the system and provides enhanced explainability and interpretability of results over popular machine-learning classifiers due to the linguistic nature of fuzzy systems