Visual Sensor-Based Image Analysis of the Relationship between Nutritional Diet and Athletic Ability of Sports Dance Athletes

Sports dance originated from the international standard national standard dance. Since China formally established the “China International Ballroom Dance Association” in 1986, it has developed rapidly. At the same time, related research on sports dancers has become a hot spot. However, most of the current researches are limited to athletes’ physical training and competitive skills, and the research on athletes’ dietary nutrition and athletic ability is still blank. In response to this situation, this paper presents a study on the relationship between nutritional diet and athletic ability of sports dancers based on image analysis of visual sensors. This article is mainly divided into three parts. The first part is the basic theories and core concepts of related research. This part introduces the modes and algorithms of visual sensors, analyzes the specific problems of current sports dance athletes in my country in terms of dietary nutrition, and continues with the adverse effects. It affects the athlete’s athletic ability and even threatens the athlete’s health. Improving the diet of athletes and establishing healthy dietary standards are of great significance to the development of this field. The second part is the establishment method of the nutritional intervention experiment model, which gives the principle and specific operation steps of the experiment design in detail. The third part is a comparative test. To further confirm the influence of nutrition on dance performance in athletes, this article conducted a number of comparative studies such as immunoglobulin conversion after intervention, changes in the indicators of lipid metabolism after intervention, and dietary mineral intake. Through the analysis of experimental data, it can be seen that reasonable dietary supplementation has a positive effect on the recovery of athletes’ physical fitness, which can enhance the body’s immunity while improving athletic ability.

Research Based on Visual Sensors and VR in the Field of Visual Culture

Visual sensors provide us with a wealth of multimedia data; one of the core parts of VR technology is to present people with a real and immersive simulation environment; the application of this technology makes the human narrative to achieve visual transformation and further strengthens the central position of visual culture. This paper studies the field of visual culture from two aspects of visual sensor and VR. In this paper, the HSV color model models task processing problems in visual sensor networks using separable load theory and studies ways to seek optimal task scheduling strategies to minimize the completion time of the visual task. This paper mainly uses literature research method, questionnaire survey method, and statistical analysis method to design a model image output process to explore the application of visual culture under the development of visual sensor and VR and analyzes the development status of film, game, and cultural tourism industry VR in the field of visual culture. Questionnaire data show that compared with traditional video games, more people chose games under the application of VR technology for entertainment; in VR game experience, more than 70% are satisfied with game color and style, operation form, scene design, and experience of VR game itself, which shows to some extent the role of visual sensor and VR in visual culture.

Document-Image Related Visual Sensors and Machine Learning Techniques

Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g., libraries, office communication, managementof workflows, and electronic archiving [...]

SLAM-OR: Simultaneous Localization, Mapping and Object Recognition Using Video Sensors Data in Open Environments from the Sparse Points Cloud

In this paper, we propose a novel approach that enables simultaneous localization, mapping (SLAM) and objects recognition using visual sensors data in open environments that is capable to work on sparse data point clouds. In the proposed algorithm the ORB-SLAM uses the current and previous monocular visual sensors video frame to determine observer position and to determine a cloud of points that represent objects in the environment, while the deep neural network uses the current frame to detect and recognize objects (OR). In the next step, the sparse point cloud returned from the SLAM algorithm is compared with the area recognized by the OR network. Because each point from the 3D map has its counterpart in the current frame, therefore the filtration of points matching the area recognized by the OR algorithm is performed. The clustering algorithm determines areas in which points are densely distributed in order to detect spatial positions of objects detected by OR. Then by using principal component analysis (PCA)—based heuristic we estimate bounding boxes of detected objects. The image processing pipeline that uses sparse point clouds generated by SLAM in order to determine positions of objects recognized by deep neural network and mentioned PCA heuristic are main novelties of our solution. In contrary to state-of-the-art approaches, our algorithm does not require any additional calculations like generation of dense point clouds for objects positioning, which highly simplifies the task. We have evaluated our research on large benchmark dataset using various state-of-the-art OR architectures (YOLO, MobileNet, RetinaNet) and clustering algorithms (DBSCAN and OPTICS) obtaining promising results. Both our source codes and evaluation data sets are available for download, so our results can be easily reproduced.

Drones as internet of video things front-end sensors: challenges and opportunities

Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.

Target Tracking in Wireless Visual Sensor Networks: Challenges, Steps, and Metrics of Evaluation

In recent years, wireless sensor networks have been used in a wide range of applications such as smart cities, military, and environmental monitoring. Target tracking is one of the most interesting applications in this area of research, which mainly consists of detecting the targets that move in the area of interest and monitoring their motions. However, tracking a target using visual sensors is different and more difficult than that of scalar sensors due to the special characteristics of visual sensors, such as their directional limited field of view, and the nature and amount of the sensed data. In this paper, we first present the challenges of detection and target tracking in wireless visual sensor networks, then we propose a scheme that describes the basic steps of target tracking in these networks, we focus then on the tracking across camera nodes by presenting some metrics that can be considered when designing and evaluating this type of tracking approaches.