A New Hybrid Architecture for Human Activity Recognition from RGB-D videos

International audienceActivity Recognition from RGB-D videos is still an open problem due to the presence of large varieties of actions. In this work, we propose a new architecture by mixing a high level handcrafted strategy and machine learning techniques. We propose a novel two level fusion strategy to combine features from different cues to address the problem of large variety of actions. As similar actions are common in daily living activities, we also propose a mechanism for similar action discrimination. We validate our approach on four public datasets, CAD-60, CAD-120, MSRDailyActivity3D, and NTU-RGB+D improving the state-of-the-art results on them

Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition

Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte

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

Detección de acciones humanas a partir de información de profundidad mediante redes neuronales convolucionales

El objetivo principal del presente trabajo es la implementación de un sistema de detección de acciones humanas en el ámbito de la seguridad y la video-vigilancia a partir de la información de profundidad ("Depth") proporcionada por sensores RGB-D. El sistema se basa en el empleo de redes neuronales convolucionales 3D (3D-CNN) que permiten realizar de forma automática la extracción de características y clasificación de acciones a partir de la información espacial y temporal de las secuencias de profundidad. La propuesta se ha evaluado de forma exhaustiva, obteniendo como resultados experimentales, una precisión del 94% en la detección de acciones. Si tenéis problemas, sugerencias o comentarios sobre el mismo, dirigidlas por favor a Sergio de López Diz .The main objective of this work is the implementation of human actions detection system in the field of security and video-surveillance from depth information provided by RGB-D sensors. The system is based on 3D convolutional neural networks (3D-CNN) that allow the automatic features extraction and actions classification from spatial and temporal information of depth sequences. The proposal has been exhaustively evaluated, obtaining as experimental results, an accuracy of 94% in the actions detection. If you have problems, suggestions or comments on the document, please forward them to Sergio de López Diz .Grado en Ingeniería Electrónica de Comunicacione