Action Recognition, Tracking, and Optimization Analysis of Training Process Based on the Support Vector Regression Model.

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.

In order to improve the accuracy of human action recognition and accelerate the recognition speed, we propose a method for human action recognition by modeling the human primary visual cortex neurons. The method firstly extracted motion information by using 3DGabor spatial-temporal filters to model the classical receptive field (CRF) of simple cells in the primary visual cortex. Secondly, conductance-driven integrate and fire neuron model was used to simulate the primary visual cortex neuron, by which motion information was converted into spike train. Finally, the mean firing rate of spike train formed a feature vector that captures the characteristic of human actions in this video sequence. Using Support Vector Machine (SVM), the method is tested on the Weizmann action dataset. The obtained impressive results show that our method was more effective than model of Escobar in human action recognition.

The article considers the problem of undefined complex classes activity recognition. The human activity recognition relevance in the field of production and services is presented. The classification of physical activity types is described. The possibilities of using methods based on data and knowledge for human activity recognition are considered. An analysis of zero-shot learning is given. A functional model of the method for undefined complex human activity recognition is proposed. The datasets are described. The HH101 and HH105 datasets obtained from CASAS smart homes were used to conduct experimental studies. An experimental study of the developed method for human activity recognition is carried out.

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.

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation.

This paper presents some more recent developments in the research area of human activity recognition. In many cases, human activity recognition can be achieved with the support of body sensors that deliver information about the motion of a person or about acceleration and position. Such data can contribute substantially to the accuracy of the results, however wearing extra equipment is not a very popular practice for most human users and therefore camera-based methods are mostly preferred in today’s systems, which is also a result of the tremendous progress achieved in recent years in pattern recognition and machine learning. Therefore, also this paper concentrates on camera-based methods for human activity recognition. Activity recognition has a large variety of sub-research areas and has many exciting application areas, such as e.g. interaction in smart spaces, surveillance, human-robot interaction or automatic analysis of interest, emotions and human traits.

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.

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.

With the rise of computer vision, it is becoming more and more important to accurately recognize and evaluate human actions. However, the complexity, intraclass differences, and viewing angle changes of human actions significantly impact the accuracy of identification human actions. This paper proposed an action recognition and evaluation method based on skeleton information extraction. Briefly, We use Lightweight OpenPose to extract the key points of human skeleton and perform processing work, including video data cutting, deleting some key points, supplementing missing key points, filtering processing, feature extraction, etc. Through an in-depth exploration of related theoretical technologies, we proposed a model for recognition and evaluation of human table tennis actions with an ordinary camera. The support vector machine algorithm (SVM) classification model is used to identify table tennis actions in real-time. Then the dynamic time regularization (DTW) algorithm calculates the similarity of each human skeleton key point in the action sequence. The low-scoring bone key points are marked to evaluate the human table tennis action in real time. The results show that a recognition rate of more than 95% is achieved in the test set, which proves the method's effectiveness. In addition, we compared the results with previous work using inertial sensors for action recognition, which shows our method can preserve the same accuracy with a much lower cost of implementation.

SUMMARYRemote surveillance of large‐scale equipment such as power plants and building complexes is important to prevent serious attacks and troubles. Automatic human action recognition can reduce the burdens of the surveillance. Multiview video is useful for human action recognition, because it provides robustness to the changes of people's appearance by orientation and occlusion. One problem of conventional multiview action recognition is that it requires both detection and tracking before action recognition. Human pose and motion vary depending on the person's action, and such variances may cause detection and tracking error. To solve this problem, previous work has proposed simultaneous action recognition and location estimation for single‐view videos using Hough voting. In this paper, we extend the Hough voting approach to simultaneous multiview action recognition and location estimation. Our proposed method independently casts votes for the action labels and spatiotemporal reference positions of actions in each view and integrates them using homographical transformations in the multiview extension. We evaluated our method and confirmed that it achieved high accuracy in action recognition and location estimation. The contribution of this paper is that it enables multiview action recognition without prior human detection and tracking.

In this paper, we present an original and efficient method of human action recognition in a video sequence. The proposed model is based on the generation and fusion of a set of prototypes generated from different view-points of the data cube of the video sequence. More precisely, each prototype is generated by using a multidimensional scaling (MDS) based nonlinear dimensionality reduction technique both along the temporal axis but also along the spatial axis (row and column) of the binary video sequence of 2D silhouettes. This strategy aims at modeling each human action in a low dimensional space, as a trajectory of points or a specific curve, for each viewpoint of the video cube in a complementary way. A simple K-NN classifier is then used to classify the prototype, for a given viewpoint, associated with each action to be recognized and then the fusion of the classification results for each viewpoint allow us to significantly improve the recognition rate performance. The experiments of our approach have been conducted on the publicly available Weizmann data-set and show the sensitivity of the proposed recognition system to each individual viewpoint and the efficiency of our multi-viewpoint based fusion approach compared to the best existing state-of-the-art human action recognition methods recently proposed in the literature.

In order to improve the accuracy of human action recognition in video and the computational efficiency of large data sets, an action recognition algorithm based on multiple features and modified deep learning model is proposed. First, the deep network pre-training process is used to learn and optimize the RBM parameters, and the deep belief nets (DBN) model is constructed through deep learning. Then, human 13 joint points and critical points of optical flow are automatically extracted by DBN model. Second, these more abstract and more effective human motion features are combined to represent human actions. Ultimately, the entire DBN network structure is fine-tuned by support vector machine (SVM) algorithm to classify human actions. We demonstrate that human 13 joint points and critical points of optical flow are two very effective human action characterizations, our proposed approach greatly reduces the required samples, and shortens the training time of the samples, can efficiently process large data sets and can effectively recognize novel actions. We performed experiments on the KTH data set, Weizmann data set, the ballet data set and UCF101 data set to evaluate the proposed method, the experiment results show that the average recognition accuracy is over 98%, which validates its effectiveness, and show that our results are stable, reliable, and significantly better than the results of two state-of-the-art approaches on four different data sets. So, it lays a good theoretical foundation for practical applications.

The recognition of human activities using vision-based techniques has become a crucial research field in video analytics. Over the last decade, there have been numerous advancements in deep learning algorithms aimed at accurately detecting complex human actions in video streams. While these algorithms have demonstrated impressive performance in activity recognition, they often exhibit a bias towards either model performance or computational efficiency. This biased trade-off between robustness and efficiency poses challenges when addressing complex human activity recognition problems. To address this issue, this paper presents a computationally efficient yet robust approach, exploiting saliency-aware spatial and temporal features for human action recognition in videos. To achieve effective representation of human actions, we propose an efficient approach called the dual-attentional Residual 3D Convolutional Neural Network (DA-R3DCNN). Our proposed method utilizes a unified channel-spatial attention mechanism, allowing it to efficiently extract significant human-centric features from video frames. By combining dual channel-spatial attention layers with residual 3D convolution layers, the network becomes more discerning in capturing spatial receptive fields containing objects within the feature maps. To assess the effectiveness and robustness of our proposed method, we have conducted extensive experiments on four well-established benchmark datasets for human action recognition. The quantitative results obtained validate the efficiency of our method, showcasing significant improvements in accuracy of up to 11% as compared to state-of-the-art human action recognition methods. Additionally, our evaluation of inference time reveals that the proposed method achieves up to a 74× improvement in frames per second (FPS) compared to existing approaches, thus showing the suitability and effectiveness of the proposed DA-R3DCNN for real-time human activity recognition.

Exercise is an indispensable part of people’s lives and is closely related to their health. Human Activity Recognition (HAR), which involves detects and analyzes human body activity, has become the focus of current research. Photoplethysmography (PPG) has advantages such as convenience for detection and low cost, and is widely used in wearable devices becoming an ideal choice for HAR. In this study, we used wavelet scattering transform (WST) to extract features from PPG and then performed activity recognition on it. We achieved excellent classification accuracy of 92.54% and 97.76% respectively in the experiments of three-class and four-class exercise detection. The results showed this method based on wavelet scattering transform and PPG can accurately detect exercise types and provide effective support for HAR.