Spatial image polynomial decomposition with application to video classification

An automatic human action representation and recognition technique is proposed in this paper. Appearance-change problem due to human wearing dresses and body shapes is also investigated in this study for automatic human action recognition. A tuned eigenspace technique is proposed for automatic human posture and/or motion recognition that successfully overcome the preceding problems. We employ image pre-processing by Gaussian and Sobel edge filter, called the first stage tuning, for reducing a dress effect, and a mean eigenspace produced by taking a mean of the similar postures, called the second stage tuning, for avoiding the preceding problems. An eigenspace called a tuned eigenspace is obtained from the mentioned processes and it is used for further recognition of unfamiliar postures and actions. The proposed method is compared with a related technique and the robustness of this approach is presented.

In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.

Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a whole-motion model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.

Human Activity Recognition (HAR) is widely used in many applications and HAR using smartphone only has been proved to be effective, flexible and unobtrusive for activity recognition. In this paper, a two-layer and multi-strategy HAR framework is proposed to overcome the major challenge of HAR using smartphone only, i.e., the variation in orientation and position of the device. In the first layer, the activities are classified into different groups with high accuracy and for each group in the second layer, the appropriate strategy is designed according to the characteristics of the group to improve the recognition performance. For static activity group, the transitional activities are introduced to help classifying the activities indirectly. For dynamic activity group sensitive to the position variation of the smartphone, a position-assisted strategy is proposed to alleviate the influence of position variation. The simulation results demonstrate the effectiveness of the proposed two-layer multi-strategy HAR framework.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

Human Activity Recognition (HAR) is pivotal in ubiquitous computing, offering benefits to human-centric services such as health monitoring, smart homes, and eldercare systems. HAR leverages smartphones, smartwatches, and other wearable devices to collect sensory data annotated with activity labels, which are then used to train machine learning or deep learning models for automatic activity recognition. Effective HAR systems must integrate information from multiple modalities to accurately assist users. This paper introduces the XAttentionHAR model, an innovative cross-modality attention-based ensemble model for HAR. Our approach utilizes a self-attention module to extract features within each modality and an inter-domain cross-attention module to capture and integrate long-term dependencies across domains. The cross-modality attention mechanism enhances the fusion of diverse modalities, enriching the semantic information. We conducted extensive experiments on the WISDM public dataset, which includes accelerometer and gyroscope data from smartwatches and smartphones. Our results demonstrate that XAttentionHAR outperforms other state-of-the-art methods in activity recognition, achieving 98.48% accuracy for smartphone-based HAR and 98.73% accuracy for smartwatch-based HAR, paving the way for improved human-centric services.

Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.

Scene image and human skeleton-based dual-stream human action recognition

Human detection and action recognition form the basis for understanding human behaviors. Human detection is used to detect the positions of humans, and action recognition is able to recognize the action of specific humans. However, numerous approaches have been used to handle action recognition and human detection separately. Therefore, three main issues still exist when independent methods of human detection and action recognition are combined, 1) intrinsic errors in object detection impact the performance of action recognition, 2) features common to action recognition and object detection are missed, 3) the combination also has an impact on processing speed. We propose a single framework for human detection and action recognition to solve these issues. It is based on a hierarchical structure called Boosted Randomized Trees. The nodes are trained such that the upper nodes detect humans from the background, while the lower nodes recognize action. We were able to improve both human detection and action recognition rates over earlier hierarchical structure approaches with the proposed method.

In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human activity recognition (HAR) using heterogeneous sensor data in a smart home. HAR assists artificial intelligence in providing services to users according to their preferences. The sync-LSTM algorithm improves learning performance and sees its potential for real-world applications in complex HAR, such as concurrent activity, with higher accuracy and satisfactory computational complexity. The adapted algorithm for HAR is also applicable in the fields of ambient assistive living, healthcare, robotics, pervasive computing, and astronomy. Extensive experimental evaluation with publicly available datasets demonstrates the competitive recognition capabilities of our approach. The sync-LSTM algorithm improves learning performance and has the potential for real-life applications in complex HAR. For concurrent activity recognition, our proposed method shows an accuracy of more than 97%.

Video-based human action recognition is one of the most important and challenging areas of research in the field of computer vision. Human action recognition has found many pragmatic applications in video surveillance, human-computer interaction, entertainment, autonomous driving, etc. Owing to the recent development of deep learning methods for human action recognition, the performance of action recognition has significantly enhanced for challenging datasets. Deep learning techniques are mainly used for recognizing actions in images and videos comprising of Euclidean data. A recent development in deep learning methods is the extension of these techniques to non-Euclidean data or graph data with many nodes and edges. Human body skeleton resembles a graph, therefore, the graph convolutional network (GCN) is applicable to the non-Euclidean body skeleton. In the past few years, GCN has emerged as an important tool for skeleton-based action recognition. Therefore, we conduct a survey using GCN methods for action recognition. Herein, we present a comprehensive overview of recent GCN techniques for action recognition, propose a taxonomy for the categorization of GCN techniques for action recognition, carry out a detailed study of the benchmark datasets, enlist relevant resources and open-source codes, and finally provide an outline for future research directions and trends. To the best of authors' knowledge, this is the first survey for action recognition using GCN techniques.

Human action recognition is challenging, due to large temporal and spatial variations in actions performed by humans. These variations include significant nonlinear temporal stretching. In this paper, we propose an intuitively simple method to extract action templates from 3D human joint data that is insensitive to nonlinear stretching. The extracted action templates are used as the training instances of the actions to train multiple classifiers including a multi-class SVM classifier. Given an unknown action, we first extract and classify all its constituent atomic actions and then assign the action label via an equal voting scheme. We have tested the method on two public datasets that contain 3D human skeleton data. The experimental results show the proposed method can obtain a comparable or better performance than published state-of-the-art methods. Additional experiments also demonstrate the method works robustly on randomly stretched actions.

Emotions and activities are an integral part of human lifestyles and daily living. They occur all the time in everyone as humans are always in an emotional state and also performing an activity. This paper proposes an architecture that combines Human Emotion Recognition (HER) and Human Activity Recognition (HAR) to monitor people. It utilizes emotion physiological signals and accelerometer data harvested from individuals to recognize human emotions and activities respectively in addition to location information in order to remotely monitor such individuals and trigger alerts if necessary especially in a distress state situation that requires emergency services. The basic concepts of accelerometer data based HAR and physiological signals based HER and how they can be combined to monitor individuals in an emergency response system are succinctly presented.

Smartphones are increasing ubiquitously due to the need and demand in the modern era. The world is transmuting into a global village with the cumulation of smart devices. Nowadays, smartphones are enriched with inertial sensors which can be used to recognize physical human activities in the wild. Human activity recognition (HAR) lies at the core of many applications like health monitoring, fall detection, road safety, personal assistance, and behavior-based context awareness. Context-based HAR is a new dimension that provides fine-grained information about the action being performed and leads us towards the automated and intelligent system design, which is useful to furnish the smart solution to real-life problems. In this paper, smartphone sensors are utilized to propose a framework for human activity and context recognition. This framework performs activity-dependent context recognition on the ExtraSensory dataset using random forest, decision tree, and k-nearest neighbor classifiers, and achieves an accuracy of 93.10%, demonstrating the effectiveness of the proposed framework.

Human activity recognition is concerned with identifying the specific movement of a person based on sensor data. In recent years, many different techniques have been proposed for modelling and recognising human activities, with a specific focus on the development of approaches to classifying human activities using deep learning techniques. The research presented in this paper proposes a fuzzy feature representation approach to represent occupancy sensor data, along with a Convolutional Neural Network Classifier (CNNC) for human activity modelling and recognition. Sensory data gathered from a home environment are converted into occupancy data representing human activities and then fuzzified before being fed as inputs into the CNNC. The learning capability of CNNC allows the model to learn the relationship between the fuzzified inputs and their corresponding output activities during training mode. The relations learned in the trained CNNC model are then used to identify human activity patterns and classify these when the testing dataset is applied. The proposed method is evaluated using a dataset representing activities of daily living for a single user gathered from a real-home environment.