Aerobics Exercise Posture Tracking and Recognition System Based on Wireless Smart Sensors

Wireless sensors are a new, high-end, and popular exploration technology. The positioning and tracking of the human body are two important research issues in sensors. Aerobics is a widely popular sports item that is well-loved by the general public and integrates gymnastics, dance, music, fitness, and entertainment. The application of smart sensors helps to improve the coordination and flexibility of movements. In order to deeply study whether wireless smart sensors can play a role in tracking and recognizing aerobics exercise postures, this article uses sensor design methods, motion analysis, and software and hardware equipment architecture methods to collect samples, analyze smart sensors, and streamline algorithms. And it creates a sensor model and system for posture tracking and recognition. When testing the best position for the placement of the sensor, first fix the sensor at a distance of 2.5 cm from the wrist section and at the middle of the lower arm, keep the upper arm still, and do the stretching and contraction movements of the lower arm. Repeat the exercise 5 times and measure the wrist. The movement curve of the juncture, the result shows that the measured juncture curve at a distance of 2.5 cm is basically the same as the actual winding curve, and the fixed position in the middle deviates more from the normal curve point, and its average error is within 0.9 cm, which is correct. To test the effectiveness of the algorithm again, a total of 8 volunteers’ data were collected during the experiment, and the duration was more than 25 seconds, and each normal posture tried to maintain stability and lasted for about 6 seconds. The statistical accuracy rate is 90.6%. It shows that the algorithm or the system designed in this paper has extremely high accuracy. It is basically realized that from the theory of wireless sensor network, a system model that can be used for posture tracking and recognition of aerobics is designed.

Athlete Behavior Recognition Technology Based on Siamese-RPN Tracker Model

With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes’ trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes’ behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete’s behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes’ target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes’ background environment. It can quickly capture the characteristic points of athletes’ behavior as the data input of the tracking model, so it has excellent popularization and application value.

Recognition of Hybrid Graphic-Text License Plates

Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.

Image Dehazing and Enhancement: A Review

The enhancement of images is an image processing method that highlights certain image information according to specific needs and, at the same time, weakens or removes unwanted information. In the field of computer and machine vision, haziness and fog lead to degradation of images using different degradation mechanisms, including contrast attenuation, blurring, and degradation of the pixels. This limits machine vision systems efficiency such as video monitoring, target tracking, and recognition. Different dark channel single image dehazing algorithms have been designed quickly and efficiently to address image hazing problems. These algorithms rely on the dark channel theory to estimate the atmospheric light which is a crucial dehazing parameter. In this paper, a review of image dehazing and enhancement has been presented.

A review of human skin detection applications based on image processing

In computer science, virtual image processing is the use of a digital computer to manipulate digital images through an algorithm for many applications. To begin with a new research topic, the must trend application that gets many requests to develop should know. Therefore, many applications based on human skin and human life are reviewed in this article, such as detection, classification, blocking, cryptography, identification, localization, steganography, segmentation, tracking, and recognition. In this article, the published articles with the topic of human skin-based image processing are investigated. The international publishers, such as Springer, IEEE, arXiv, and Elsevier are selected. The searching is implemented with the duration criteria of 2015-2019. It noted that human skin detection and recognition are the most repetitive articles with 43% and 28.5%, respectively of the total number of the investigated articles. The usage of human skin models is being widely used in the image processing of various applications.