Vision Based Activity Recognition Using Machine Learning and Deep Learning Architecture

Human Activity recognition, with wide application in fields like video surveillance, sports, human interaction, elderly care has shown great influence in upbringing the standard of life of people. With the constant development of new architecture, models, and an increase in the computational capability of the system, the adoption of machine learning and deep learning for activity recognition has shown great improvement with high performance in recent years. My research goal in this thesis is to design and compare machine learning and deep learning models for activity recognition through videos collected from different media in the field of sports. Human activity recognition (HAR) mostly is to recognize the action performed by a human through the data collected from different sources automatically. Based on the literature review, most data collected for analysis is based on time series data collected through different sensors and video-based data collected through the camera. So firstly, our research analyzes and compare different machine learning and deep learning architecture with sensor-based data collected from an accelerometer of a smartphone place at different position of the human body. Without any hand-crafted feature extraction methods, we found that deep learning architecture outperforms most of the machine learning architecture and the use of multiple sensors has higher accuracy than a dataset collected from a single sensor. Secondly, as collecting data from sensors in real-time is not feasible in all the fields such as sports, we study the activity recognition by using the video dataset. For this, we used two state-of-the-art deep learning architectures previously trained on the big, annotated dataset using transfer learning methods for activity recognition in three different sports-related publicly available datasets. Extending the study to the different activities performed on a single sport, and to avoid the current trend of using special cameras and expensive set up around the court for data collection, we developed our video dataset using sports coverage of basketball games broadcasted through broadcasting media. The detailed analysis and experiments based on different criteria such as range of shots taken, scoring activities is presented for 8 different activities using state-of-art deep learning architecture for video classification

Skin detection by dual maximization of detectors agreement for video monitoring

This is the author’s version of a work that was accepted for publication in Journal Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Pattern Recognition Letters, 34, 16 (2013) DOI: 10.1016/j.patrec.2013.07.016This paper presents an approach for skin detection which is able to adapt its parameters to image data captured from video monitoring tasks with a medium field of view. It is composed of two detectors designed to get high and low probable skin pixels (respectively, regions and isolated pixels). Each one is based on thresholding two color channels, which are dynamically selected. Adaptation is based on the agreement maximization framework, whose aim is to find the configuration with the highest similarity between the channel results. Moreover, we improve such framework by learning how detector parameters are related and proposing an agreement function to consider expected skin properties. Finally, both detectors are combined by morphological reconstruction filtering to keep the skin regions whilst removing wrongly detected regions. The proposed approach is evaluated on heterogeneous human activity recognition datasets outperforming the most relevant state-of-the-art approaches.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo)

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader