“…e main research contents of this paper include target intelligent tracking attitude reconstruction based on contour matching; intelligent target tracking attitude refinement based on video content; human motion reconstruction combined with spatiotemporal model of motion library [17]. On this basis, a video based 3D human motion generation platform V hsportstrackingver 1.0 is realized.…”
Section: Systemmentioning
confidence: 99%
“…e first is to promote the acquisition of action goals. Because additional feedback provides information about the success of skill operation, learners can determine the appropriate activity content in order to operate the skills correctly [3]. In this way, compared with not obtaining any external information, additional feedback can help individuals achieve skill goals faster and easier.…”
In order to better improve the martial arts learning effect, this paper puts forward the design method of martial arts motion feedback system based on target intelligent tracking video processing, optimizes the hardware configuration of the system, adds a composite tracker to the system, so as to effectively track the motion changes of different human targets, and further combines the real-time target intelligent tracking video processing technology and builds a moving target tracking model; then, based on the calculation of motion characteristic parameters, CRF method is used for motion behavior recognition and feedback. Finally, simulation studies reveal that a martial arts motion feedback system based on real-time target intelligent tracking video processing can track moving targets more accurately and has the potential for practical implementation.
“…e main research contents of this paper include target intelligent tracking attitude reconstruction based on contour matching; intelligent target tracking attitude refinement based on video content; human motion reconstruction combined with spatiotemporal model of motion library [17]. On this basis, a video based 3D human motion generation platform V hsportstrackingver 1.0 is realized.…”
Section: Systemmentioning
confidence: 99%
“…e first is to promote the acquisition of action goals. Because additional feedback provides information about the success of skill operation, learners can determine the appropriate activity content in order to operate the skills correctly [3]. In this way, compared with not obtaining any external information, additional feedback can help individuals achieve skill goals faster and easier.…”
In order to better improve the martial arts learning effect, this paper puts forward the design method of martial arts motion feedback system based on target intelligent tracking video processing, optimizes the hardware configuration of the system, adds a composite tracker to the system, so as to effectively track the motion changes of different human targets, and further combines the real-time target intelligent tracking video processing technology and builds a moving target tracking model; then, based on the calculation of motion characteristic parameters, CRF method is used for motion behavior recognition and feedback. Finally, simulation studies reveal that a martial arts motion feedback system based on real-time target intelligent tracking video processing can track moving targets more accurately and has the potential for practical implementation.
“…Errors often arise when manually identifying wood types, often due to a lack of experience and knowledge about wood [10]. Moreover, the direct visual examination of wood reveals nearly indistinguishable patterns and textures, necessitating prolonged and repetitive identification procedures to ensure accuracy [11]. The development of technology capable of analyzing wood textures to differentiate and classify wood types has become imperative.…”
Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.
The small footprint of tiny multirotor vehicles is advantageous for accessing tight spaces, but their limited payload and endurance impact the ability to carry powerful sensory and computing units for navigation. This article reports an aerodynamics-based strategy for a ducted rotorcraft to avoid wall collisions and explore unknown environments. The vehicle uses the minimal sensing system conventionally conceived only for hovering. The framework leverages the duct-strengthened interaction between the propeller wake and vertical surfaces. When incorporated with the flight dynamics, the derived momentum-theory-based model allows the robot to estimate the obstacle’s distance and direction without range sensors or vision. To this end, we devised a flight controller and reactive navigation methods for the robot to fly safely in unexplored environments. Flight experiments validated the detection and collision avoidance ability. The robot successfully identified and followed the wall contour to negotiate a staircase and evaded detected obstacles in proof-of-concept flights.
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