Search

The emergence of data-driven analytics and intelligent monitoring systems is radically transforming the world of competitive sports. However, robust real-time activity recognition remains elusive for rapid-paced sports like table tennis due to the complexity of strokes and quick maneuvering. This paper tries to overcome this challenge through an innovative cloud-supported system integrating the Internet of Things (IoT), machine learning, and wearable sensors for automated analysis of table tennis gameplay. We strategically set up a multi-camera IoT system around a table tennis court to compile a rich labeled image dataset encapsulating over 15,000 frames. To improve model generalization, the images captured various playing styles, lighting conditions, and camera angles. We developed tailored SVM and CNN architectures optimized for table tennis activity classification. The models were trained on GPU-accelerated platforms using the curated dataset. After hyperparameter tuning and cross-validation, the CNN model achieved 96.2% accuracy, a precision value of 96.3%, a recall value of 95.2%, and an F1-score of 96.3% in classifying standard strokes and serves. Additionally, the models exhibited impressive processing speeds of 22–34 fps, enabling real-time utilization. Our proposed model outperformed the latest models in this field with higher accuracy, recall values, F1-score, and precision. While expanding the dataset diversity and testing variations of deep network architectures could further enhance performance, this paper demonstrates a crucial leap toward helping real-time analytics in table tennis using an AI-powered computer vision approach. The hybrid system combining IoT, wearables, and machine learning establishes a framework to transform data into actionable and timely insights for table tennis players and coaches. [ABSTRACT FROM AUTHOR]

In recent years, developments in the fields of computer vision and artificial intelligence have created new opportunities for studying sports performance. With advancements in computer vision and artificial intelligence, it is now possible to use massive volumes of video data to get deeper insights into sports dynamics, especially in precision-based video sports like table tennis. To develop skills, a thorough examination of player movements is required. With the development of vision-based human posture recognition, computers can function like humans and derive intelligent judgments from outside data. This paper presents a novel method that uses graph convolutional neural networks (GCNNs) to detect and identify improper postures and movements in table tennis players. In addition to conventional methods, the proposed method dissects the human skeleton into finely detailed head, trunk, and leg features. Deep-level features that offer a more comprehensive understanding of athlete movements are extracted after feeding these features into the network. The softmax classifier combines these features to produce the final recognition result. The effectiveness of this approach has been evaluated through extensive experimentation. The GCN model performs remarkably well, with accuracy rates of 86.4% on the NTU-RGB + D dataset and 79.1% on the COCO dataset. This accomplishment is significant because the model consistently yields accurate detection results, even in complex and occlusion-filled scenes. In comparison tests, the proposed model performs better than GraphSAGE, GAT, ChebNet, GIN, and GC-LSTM in identifying relevant body part movements in table tennis players while ignoring irrelevant ones. According to proposed experiments, this enhances the recognition of improper postures in most table tennis actions. [ABSTRACT FROM AUTHOR]

In recent times, the neural image captioning (NIC) is considered as a primitive problem artificial intelligence (AI) in which creates a connection between computer vision (CV) and natural language processing (NLP). However, recent attribute-based and textual semantic attention-based models in NIC still encounter challenges related to irrelevant concentration of the designed attention mechanism on the relationship between extracted visual features and textual representations of corresponding image's caption. Moreover, recent NIC-based models also suffer from the uncertainties and noises of extracted visual latent features from images which sometime leads to the disruption of the given image captioning model to sufficiently attend on the correct visual concepts. To solve these challenges, in this paper, we proposed an end-to-end integrated deep fuzzy-neural network with the unified attention-based semantic-enhanced vision-language approach, called as FuzzSemNIC. To alleviate noises and ambiguities from the extracted visual features, we apply a fused deep fuzzy-based neural network architecture to effectively learn and generate the visual representations of images. Then, the learnt fuzzy-based visual embedding vectors are combined with selective attributes/concepts of images via a recurrent neural network (RNN) architecture to incorporate the fused latent visual features into captioning task. Finally, the fused visual representations are integrated with a unified vision-language encoder–decoder for handling caption generation task. Extensive experiments in benchmark NIC-based datasets demonstrate the effectiveness of our proposed FuzzSemNIC model. [ABSTRACT FROM AUTHOR]

One of the key components of artificial intelligence algorithms is machine learning, involving a variety of fields, and has been applied in many artificial intelligence systems, including computer vision algorithm, radio network algorithm, medical diagnosis algorithm and intelligent robot system algorithm. In the form of machine learning algorithm, the machine learning module of the algorithm is first used to calculate the consumption, the main performance modules are optimized and improved, and the system data under database optimization are obtained, and selected the optimized structure of the database for calculation, data analysis for the calculation results. Finally, in the design of the database optimization system, the separation of the database system storage engine is studied, and the database optimization form under the data processing structure is proposed. In terms of performance and functionality and reliability, it helps to solve the loss problem caused by big data processing in transmission. In the research of data intensive downward moving calculation process, the optimized solution of data processing is estimated. The results show that using computer terminal sampling comparison to select the executable data processing scheme. The result of this paper shows that it can improve the calculation efficiency of data optimization system query. [ABSTRACT FROM AUTHOR]

Usage of sketches for offender recognition has turned out to be one of the law enforcement agencies and defense systems' typical practices. Usual practices involve producing a convict's sketch through the crime observer's explanations. Nevertheless, researches have effectively proved the failure of customary practices as they carry a maximum level of discrepancies in the process of identification. The advent of computer vision techniques has replaced this traditional procedure with intelligent machines capable of ruling out the possible discrepancies, thus assisting the investigation process and considering the relevant points mentioned earlier. This research paper has investigated an adversarial network toward achieving color photograph images out of sketches, which are then classified using pre-trained transfer learning models to accomplish the identification process. Further, to enhance the adversarial network's performance factor in terms of photogeneration, we also employed a novel sketch generator based on the gamma adjustment technique. Experimental trials are steered with image datasets open to the research community. The trials' outcomes evidenced that the proposed system achieved the lowest similarity score of 91% and the average identification accuracy of more than 70% on all the datasets. Comparative analysis portrayed in this work also attests that the proposed technique performs ably better than any other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

We have encouraged our authors to present multidisciplinary research papers in this special issue and present papers which are directly or indirectly related to pattern recognition and computer vision. This special issue is on topics of pattern recognition and computer vision, along with their applications in different domains and systems in different environments. [Extracted from the article]