Suchergebnisse

Despite the role of working memory capacity (WMC) in decision making, there is a dearth of empirical literature concerned with working memory and how it relates to tactical decision making in sport. The temporal occlusion paradigm has often been used by sport researchers to improve tactical decision making and, thus, provides a well-established foundation for creating decision-making trainings. Therefore, the purpose of the current study was to explore the implementation of computer-based learning modules to improve the tactical decision making of four high school quarterbacks with varying levels of WMC, utilizing a single-subject, multiple baseline design. The learning modules utilized a temporal occlusion paradigm and present a novel intervention aimed at improving decision making in quarterbacks. Data were analyzed using visual analysis and improvement rate difference (IRD). Overall, results did not demonstrate a causal relationship between changes in accuracy of decision making after implementation of the learning modules but did provide moderate evidence for improvement in reaction time for decision making due to the learning modules. The learning modules were met with positive perceptions from the four participants, and the participant with the lowest WMC showed evidence of improvement in both accuracy and speed of decision making. Limitations as well as implications will be discussed.

Der Erfolg eines Video-Trainings hängt entscheidend von den richtigen Feedback-Methoden ab. Traditionelle Feedback-Methoden sind hilfreich, wenn Rückmeldung über konkret beschreibbare und überprüfbare Teilfertigkeiten gegeben werden sollen. Es gibt aber Probleme, wenn es um Einstellungs- und Bewusstseinsveränderungsprozesse geht. Hier bietet die vom Autor entwickelte FISA-Methode einen Rahmen für videounterstützte Trainings. Persönliche Veränderungsprozesse werden so tatsächlich möglich. - 1. Das Problem der "Objektivität" im Video-Training 2. Selbstkonfrontation und ihre Auswirkungen auf den Lernprozess 3. Feedback-Methoden 4. Video-Feedback nach der FISA-Methode 5. Anwendungsbereiche der FISA-MethodeBodo

This system constantly processes incoming camera footage to detect any trace of number plates. While detecting a number plate in front of the camera, it processes the camera input and extracts the number plate image from the video. Processes the extracted image and extracts the number plate number from it using easy-ocr. Thus, put forward a fully functional vehicle number plate recognition system using video based number plate recognition. Even for camera surveillance based security system, Important and difficult task is to recognize a car number plate from video. So here we come up with a solution. Extract the license plate from an image using some computer vision techniques and then use Optical Character Recognition to recognize the license number. Extraction of license plate image from car-image detection algorithms (otshu threshold method), Preprocessing-noise removal, gray scale conversion, edge detection etc., Plate extraction, Character segmentation. To design and develop a license plate detection and recognition system, will work efficiently under the conditions of slow moving objects and the objects which are merged into the background due to different traffic environment conditions, robustness against progressive or sudden illumination changes, Occlusions, identification time of the system should be as short as possible. The system should detect all the types of vehicles, recognize all the license plates of the country and should also be resistant to any kinds of disturbances, which may occur in images and mechanical plate damages which may appear in reality. The attributes of the License plates play important role in the recognition process. The size, colour of the license plate, its font face i.e. size, colour of each character, spacing between characters, the number of lines in the license plate. Video processed number plate recognition is the identification system of vehicles for collecting the toll payment. The way to identify the moving vehicle on the tollgate is by identifying the number plate which is achieved by image processing algorithm. Recognizing vehicle number plates is purely localized and difficult to follow the standard pattern assigned by the government, so the recognition is quite difficult but it is much needed system.