AI-based, automated longitudinal performance monitoring of multiple boxers in large scale videosShanmugasundaramurthi, Karthikeyan Angalamman; Baghel, Vipul; Kirupakaran, Anish Monsley; Srinivasan, Babji; Hegde, Ravi Sadananda
doi: 10.1117/12.3024133pmid: N/A
Machine vision and AI-based techniques hold significant promise for automating the analysis of extensive sports video datasets to uncover longitudinal performance trends. This study introduces an innovative pipeline tailored for the analysis of lengthy top-view boxing training session videos, recorded in uncontrolled natural settings and featuring multiple athletes. Our primary focus lies in capturing the movement patterns of boxers within the ring. Within this research, we present Histotracker, an intelligent rule-based tracking module that connects segmented objects across frames using cosine similarity. Distinguishing itself from existing trackers, this module possesses the unique ability to backtrack and correlate frames with the highest association to maintain continuous tracking information. When compared to various standard approaches, our proposed Histotracker demonstrates remarkable results, boasting a MOTA score of 0.95 In approximately 69% of the total bout videos, there were no occurrences of Identity Switching or Identity Update. These findings hold immense promise for advancing the application of automated video analytics in diverse combat sports.
Enhance routing efficiency in dynamic edge computing environments through multi-agents optimizationDoan Nguyen Thanh, Hoa; Phan Duc, Nghia; Nguyen Ngoc Thien, Phu
doi: 10.1117/12.3024968pmid: N/A
As edge computing continues to evolve, addressing the inherent challenges of low stability in edge devices becomes imperative for optimizing routing efficiency. This paper introduces a novel approach to tackle this issue through the application of Reinforcement Learning (RL) for routing optimization in dynamic edge computing environments. The core problem revolves around the need for an algorithm capable of selecting edge devices strategically to minimize latency. To validate our proposed solution, we conducted experiments utilizing Oliver30 and ry48p datasets. Comparative analysis against traditional heuristic algorithms, such as Simulated Annealing (SA) and Ant Colony Optimization (ACO), demonstrates the superior performance of our RL-based model. The results highlight the effectiveness of leveraging advanced machine learning techniques to enhance routing efficiency in challenging edge computing scenarios.
Calculation and planning of multi-beam survey lines based on geometric analysisHu, Xue; Gao, Chenying; Wu, Zifan
doi: 10.1117/12.3027427pmid: N/A
The major mainstream method for human ocean exploration is to use sound waves to measure water depth. The paper investigates the application of multi-beam bathymetric surveying technology in ocean depth measurement. Traditional single-beam sonar technology faces challenges in survey line design due to dense track lines and lack of data. To address the challenges of multi-beam survey line design below the sea surface, this paper establishes a mathematical model for calculating coverage width and overlap rate through geometric analysis of the beam’s coverage area on the sloping surface. Considering the non-isobath movement of the beam center along the sloping surface, the paper discovered the equivalent relationship between the cross-sectional angle and the horizontal plane. Therefore, a mathematical model for the depth coverage width of multi-beam survey lines was established based on the slope angle and the lateral angle. Based on previous research and relevant references, this paper designed a complete survey line scheme that can fully cover the ideal test area while maintaining a low overlap rate, and verified its rationality.
2D virtual YouTuber character generation using generative adversarial networksThedwichienchai, Natdanai; Siriborvornratanakul, Thitirat
doi: 10.1117/12.3023881pmid: N/A
Virtual YouTubers (VTubers) offer significant growth potential as a new type of content creator. However, the financial aspect poses a hurdle for aspiring VTubers. This article proposes a cost-effective solution by utilizing generative models to create full-body images of 2D VTuber characters. Notably, studies have achieved remarkable results using Generative Adversarial Networks (GANs), including Deep Convolutional GAN (DCGAN) and Style-based GAN 2 (StyleGAN2), for anime face generation. To address the lack of image synthesis systems for full-body anime characters, experiments were conducted with DCGAN and StyleGAN2 on the Danbooru dataset. The results demonstrate that StyleGAN2 models outperform DCGAN, yielding superior Fréchet Inception Distance (FID) scores of 25.06, 28.03, and 24.52, compared to DCGAN's 159.21 FID score. This research contributes to reducing the cost associated with becoming a VTuber and offers insights into generating 2D full-body anime characters for VTubers.
Hiding information in a reordered codebook using pairwise adjustments in codewordsChang, Chin-Chen; Liu, Jui-Chuan; Chang, Ching-Chun; Lin, Yijie
doi: 10.1117/12.3023916pmid: N/A
The fundamental data structure of our proposed scheme is a pixel pair. The pixel values are adjusted through simple yet well-designed equations depending on different conditions. The computation method provides an easy recovery of the original values for the pixel pair as well. For some applications, it’s important to extract the hiding data as well as to recover the original media. In order to apply to a wider range of applications, more and more reversible data hiding (RDH) schemes are introduced or enhanced in the recent decades. Therefore, the two goals of the novel scheme proposed are to increase embedding capacity and to recover original media easily. For vector quantization (VQ) compression, it’s a well-known and efficient method to compress images. A well-trained codebook is crucial to maintain the visual quality of recovered images as good as possible. An index table is generated based on the codebook to full-fill the purpose of compression. Most of studies utilize index tables as the embedding carriers for VQ or VQ-variant compression methods. There are a few state-of-the-art schemes make use of codebooks to embed secret and our proposed scheme extends the study on the codebook embedding. The codewords in a codebook are divided into pixel pairs and secret bits are embedded by adjusting the pixel values of these split pairs to form a stego codebook. During extraction, secret bits are extracted first out from the stego codebook and a recovered codebook is obtained before recovering VQ compressed images.
Front Matter: Volume 13169doi: 10.1117/12.3034699pmid: N/A
This PDF file contains the front matter associated with SPIE Proceedings Volume 13169, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.
Efficient boxing punch classification: fine-grained skeleton-based recognition made lightBaghel, Vipul; Rithihas, Nagisetti; M., Sarvanan; Srinivasan, Babji; Hegde, Ravi Sadananda
doi: 10.1117/12.3023943pmid: N/A
Sports analytics is a field of study that utilizes camera and sensor data to monitor the athlete’s performance and health to optimize the player's strategy and increase the success rate. Coaches rely on analytics to scout opponents and optimize play calls in gameplay. With the advancement in artificial intelligence, accessible and in-depth data collection has been enabled. The well-grounded technique for performance evaluation in sports analytics is Human Pose Estimation (HPE). Our focus is on real-time action recognition in combat sports like boxing. Existing state-of-the-art deep learning models are heavily parameterized, so can’t be used in real-time in any low-end devices. Apart from this, fine-grained classification in highly dynamic activities in sports are typically performed using sensors only. Our proposed Machine Learning based pipeline provides real-time fine-grained solution for 14 boxing punch types of classification using RGB video only. Our approach includes the implementation of three novel and generalized motion dynamics features that encode velocity as well as acceleration of the pose sequences., 1) Unified-Axis Angular Encoding (UAE), 2) 2D Motion Dynamics Descriptors (2DMDD), 3) Fifth-order Angular Encoding (FAE). We employed classical machine learning algorithms I.e., Support Vector Machine (SVM), Random Forest (RF), and K Nearest Neighbours (KNN) to make a lightweight model and test it on YouTube videos. The average accuracies of pipeline using the proposed features are found to be 55%, 92% and 84% for UAE, 2DMDD, and FAE respectively. Using KNN, we have achieved 99% accuracy on 10-fold cross-validation by using FAE features.
Recognition of vehicle country from license plate image based on Siamese network model with triplet loss function and negative sampling techniqueSaitov, A. Irek; Filchenkov, Andrey A.
doi: 10.1117/12.3032365pmid: N/A
The task of recognizing license plates from photographs or videos is extremely relevant nowadays. The development of transport links between cities and countries has led to the need to recognize vehicles from different regions. However, license numbers have their own characteristics for each country, such as differences in symbols, their position and font, which complicate recognition. A universal automatic recognition system must cope with these features. Our previous studies have proven the effectiveness of a model trained specifically for a target country, which for effective recognition requires an additional decision for choosing a country or region. The data used is a dataset of images of license plates mainly from countries that previously belonged to the CIS. We propose a vehicle country recognition model from license plate image based on siamese network model with triplet loss function and negative sampling technique. This model is significantly superior to a solution based on a convolutional network in terms of recognition accuracy, achieving 0.9651 value, as well as time spent on training.
Promptable model for premium connection sealing surface detection and segmentationYang, Shuai; Fan, Jianchun; Tian, Chunmeng; Han, Ting; Wei, Kaizhe
doi: 10.1117/12.3029944pmid: N/A
When applying the tubing ultrasonic testing technology to evaluate the contact condition of the metal-to-metal sealing surface of premium connection, it is necessary to judge the ultrasonic reflection amplitude image of the manual contact interface to observe whether there is sealing defect. At present, the images of phased array ultrasonic testing results usually require professionals to rely on technical knowledge to judge, the analysis has low efficiency and strong evaluation subjectivity. Therefore, there is an urgent need for an intelligent method to identify the location, range and type of sealing defects in ultrasonic images accurately and efficiently, so as assisting or replacing manual operations. Aiming at the problems of heavy reliance on data quality and inflexible segmentation effect during the process of identifying the sealing surface region using the original Mask R-CNN network, the approach described in this paper enhances the model by incorporating the Segment Anything Model(SAM) and employs prompts to guide the object detection model in generating masks that fulfill various criteria. Experiments show that the method adopted in this paper can not only correctly identify the sealing surface location, but also, compared with the original Mask R-CNN network model, it can output a segmentation mask that meets the demand according to the prompts of different segmentation criteria, and the obtained sealing surface region is closer to the theoretical segmentation region.