Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images

To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

Human gesture recognition and motion representation has become a vital base of current intelligent human-machine interfaces because of ubiquitous and more comfortable interaction. Human-Gesture recognition chiefly deals with recognizing meaningful, expressive body movements involving physical motions of face, head, arms, fingers, hands or body. This review article presents a concise overview of optimal human-gesture and motion representation of medical images. This paper surveys various works undertaken on human gesture design and discusses various design methodologies used for image segmentation and gesture recognition. It further provides a general idea of modeling techniques for analysing hand gesture images and even discusses the diverse techniques involved in motion recognition. This survey provides an insight into various efforts and developments made in the gesture/motion recognition domain through analyzing and reviewing the procedures, datasets, recognition rates and approaches employed for identifying diverse human motions and gestures for supporting better and devising improved applications in near future.

As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Human gesture recognition is a popular issue in the studies of computer vision, since it provides technological expertise required to advance the interaction between people and computers, virtual environments, smart surveillance, motion tracking, as well as other domains. Extraction of the human skeleton is a rather typical gesture recognition approach using existing technologies based on two-dimensional human gesture detection. Likewise, I t cannot be overlooked that objects in the surrounding environment give some information about human gestures. To semantically recognize the posture of the human body, the logic system presented in this research integrates the components recognized in the visual environment alongside the human skeletal position. In principle, it can improve the precision of recognizing postures and semantically represent peoples’ actions. As such, the paper suggests a potential and notion for recognizing human gestures, as well as increasing the quantity of information offered through analysis of images to enhance interaction between humans and computers.

Surface electromyography signals have significant value in gesture recognition due to their ability to reflect muscle activity in real time. However, existing gesture recognition technologies have not fully utilized surface electromyography signals, resulting in unsatisfactory recognition results. To this end, firstly, a Butterworth filter was adopted to remove high-frequency noise from the signal. A combined method of moving translation threshold was introduced to extract effective signals. Then, a gesture recognition model based on multi-stream feature fusion network was constructed. Feature extraction and fusion were carried out through multiple parallel feature extraction paths, combined with convolutional neural networks and residual attention mechanisms. Compared to popular methods of the same type, this new recognition method had the highest recognition accuracy of 92.1% and the lowest recognition error of 5%. Its recognition time for a single-gesture image was as short as 4s, with a maximum Kappa coefficient of 0.92. Therefore, this method combining multi-stream feature fusion networks can effectively improve the recognition accuracy and robustness of gestures and has high practical value.

Over the past few decades, the world has witnessed an unprecedented explosion of information. Of these, a substantial portion consists of unlabeled textual data, such as tweets, news articles, product reviews, and web snippets.
\nAs labeling is extremely expensive, time-consuming, and sometimes biased,
\nhow to effectively analyze these data becomes an imperative.
\nOwing to this, Neural Topic Models (NTMs) have emerged as a promising solution that attracts considerable research attention for their capability and interpretability.
\nThey automatically discover latent topics from unlabeled textual data through neural networks, enabling unsupervised document understanding.
\nThey have derived various downstream applications, such as content recommendation, trend analysis, and text summarization.
\nCompared to conventional topic models like LDA, NTMs offer structural flexibility and also support gradient back-propagation,
\nwhich avoids complicated model-specific derivations and well handle large-scale data.
\n
\nHowever, despite their promise, existing NTMs generally encounter several critical challenges. On the one hand, NTMs often produce low-quality topics, which are even incomparable to conventional models.
\nThese topics tend to be incoherent or repetitive, significantly diminishing their informativeness.
\nOn the other hand, NTMs struggle with low inference ability, leading to less accurate topic distributions for documents.
\nThis limitation greatly hinders document understanding and undermines the subsequent analysis, reasoning, or decision making processes.
\nDue to these challenges, existing NTMs are less useful and applicable for downstream tasks or applications.
\nAs a result, it is necessary to enhance NTMs to deliver more reliable and informative topic modeling.
\n
\nThis thesis aims to advance neural topic modeling by addressing these key challenges.
\nIn particular, we focus on four most popular scenarios: short-texts, cross-lingual, hierarchical, and basic neural topic modeling.
\nFirst, we propose a novel neural topic model tailored for short texts. 
\nThis model leverages a topic-semantic contrastive learning method to captures the similarity relations among short text samples, which works regardless of data augmentation availability.
\nThis refines short text representations, enriches learning signals, thus effectively alleviating the data sparsity issue of short texts and producing informative topics.
\n
\nSecond, we explore dynamic topic modeling.
\nWe present a neural dynamic topic model to track the evolution of dynamic topics by building the contrastive relations among them, rather than relying on the conventional Markov Chains. This model further explicitly excludes unassociated words from dynamic topics to enhance the alignment to their respective time slices.
\nThese improvements enable our model to reliably track topic evolution with high diversity.
\n
\nThird, we focus on the the affinity, rationality, and diversity of hierarchical topic modeling.
\nOur proposed neural hierarchical topic model ensures the sparsity and balance of cross-level topic dependencies using a transport plan dependency method.
\nMoreover, it distributes different semantic granularity to topics at different levels by disentangled decoding.
\nWith these, our model produces affinitive, rational, and diverse topic hierarchies.
\n
\nFourth, we tackle the topic collapsing issue and propose a basic neural topic model.
\nApart from the common reconstruction error, this model introduces a new embedding clustering regularization to force each topic embedding to be the center of a separately aggregated word embedding cluster in the semantic space.
\nThis produces topics with distinct semantics and effectively resolves the topic collapsing issue.
\n
\nFinally, we develop a comprehensive topic modeling toolkit that includes both previous conventional and cutting-edge neural topic models.
\nThis toolkit covers the complete pipelines of topic modeling, such as dataset preprocessing, model training, and evaluation.
\nThese improvements position our toolkit as a valuable resource to accelerate the research and applications of topic models.
\n
\nIn conclusion, this thesis makes significant contributions towards advancing neural topic modeling through multiple models, various scenarios, and a rigorous and comprehensive benchmark toolkit.
\nThese contributions pave the way for the utilization of neural topic modeling in diverse real-world applications.

Interacting with mobile devices can be challenging in adverse working environments. Using hand gestures for interaction can overcome severe usability issues that users face when working with mobile devices in industrial settings. This chapter is dedicated to the design, implementation, and evaluation of mobile information systems with hand gesture recognition as means for human computer interaction. The chapter provides a concise theoretical background on human gestural interaction and gesture recognition, guidelines for the design of gesture vocabularies, recommendations for proper implementation, and parameterization of robust and reliable recognition algorithms on energy-efficient 8-bit architectures. It additionally presents an elaborated process for participatory design and evaluation of gesture vocabularies to ensure high usability in the actual context of use. The chapter concludes with a case study that proves the suitability of the proposed framework for the design of efficient and reliable hand gesture-based user interfaces.

Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes. And each subtype has different treatment methods. An accurate diagnosis is the prerequisite for effective HCE treatment. However, clinicians with less diagnostic experience often make misdiagnoses of HCE and confuse its 5 subtypes in clinical practice. Computer-aided diagnosis (CAD) techniques can help clinicians to improve their diagnostic performance. This paper aims to propose an efficient CAD system that automatically differentiates 5 subtypes of HCE from the ultrasound images. The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. The experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. The overall classification accuracy from low to high was 90.46 ± 1.59% for KNN classifier, 90.92 ± 2.49% for transfer learned VGG19, and 92.01 ± 1.48% for SVM, indicating SVM classifiers with deep CNN features achieved the best performance (P < 0.05). Other performance measures used in the study are specificity, sensitivity, precision, F1-score, and area under the curve (AUC). In addition, the paper addresses a practical aspect by evaluating the system with smaller training data to demonstrate the capability of the proposed classification system. The observations of the study imply that transfer learning is a useful technique when the availability of medical images is limited. The proposed classification system by using deep CNN features and SVM classifier is potentially helpful for clinicians to improve their HCE diagnostic performance in clinical practice.

Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we propose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then we discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also incorporated the improvement of convolution kernel size, feature dimension, and network depth. Thirdly, the effects of different pooling methods, activation functions and training algorithms we proposed has been analyzed to demonstrate the advantages of the new strategy. Finally, the experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules, and the evaluation shown that our proposed method could achieve an outstanding results compare to state-of-arts.

Recognition of human actions is a topical issue due to its applicability in different domains such as security, medicine, behavioral analysis, and education. The development of the computer processing power, as well as the quality of image capture equipment, has increased the accessibility of larger and more accurate data sets. This permanently improves the performance of the developed programs. Convolutional neural networks are the starting point for identifying facial analysis and gesture recognition. By their nature, they can recognize ideal Spatio-temporal features for RGB image processing. The proposed model with 3D Kernel is useful in certain situations where the gestures are very different from each other and must be recognized by a system that reacts quickly and with high processing speed (emergencies or critical situations). The main objective of this paper is to build a reliable and easy-to-implement human gesture recognition classifier. It must be able to easily classify a large number of images so that it can operate in real-time, processing at least 30 frames per second. This is possible by using other hardware components of a shelf computer connected to a normal CCTV-Closed Circuit TV infrastructure. The selection of the gesture class must be not related to the environment, person, background, appearance, or viewing angle. The decision should be related only to the person to whom the gesture is attributed and to the context in which he reacts.KeywordsArtificial neural networksImage reconstructionKernels

Deep convolutional neural networks (CNNs) such as AlexNet, VGGNet, ResNet, EfficientNet, and MobileNet have been extensively employed in image classification tasks. A common solution is directly feeding deep CNN features extracted from a deep network into a classification function. However, this solution may easily result in poor accuracy and robustness due to the single experimental result. One alternative is utilizing an ensemble of multiple deep networks. And this would bring very expensive, even unacceptable computational complexity. Thus, we propose a new deep CNN feature ensemble frame based on multiple cross validation resampling results of the single feature layer to cope with the above two issues. Theoretically, the proposed method is proved that having a smaller error rate than the single feature layer method and the same Rademacher complexity as the single feature layer method. Moreover, extensive experiments on several challenging image classification databases demonstrate the superiority of the proposed method.

We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to encode the DCNN features. Evaluations on two challenging verification datasets show that the proposed FV-DCNN method is able to capture the salient local features and also performs well when compared to many state-of-the-art face verification methods.

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92\%, 99.18\%, and 98.76\% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recognition and sensing at extreme angles, all of which demonstrate the superior robustness and effectiveness of our system.