Design and Analysis of Interactive Multimedia Online Physical Education Platform considering Moving Object Segmentation Algorithm

With the development of Internet technology, Internet plus education has become a new mode of changing traditional education methods. Therefore, online physical education has attracted more and more attention. This paper introduces the sports object segmentation algorithm, designs an interactive multimedia online sports education platform by combining the research needs of sports online education platform, and analyzes online sports education from three aspects, sports teaching management, sports teaching resources, and sunshine sports activities, in order to improve the quality of sports education and improve students’ learning interest. Simulation results show that the algorithm is effective and can support the analysis of interactive multimedia online physical education platform.

Convolutional Neural Networks Ensembles Through Single-Iteration Optimization

Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of such models concerns their hyperparameters' setting, being highly specific for each particular problem. One common approach is to employ meta-heuristic optimization algorithms to find suitable sets of hyperparameters at the expense of increasing the computational burden, being unfeasible under real-time scenarios. In this paper, we address this problem by creating Convolutional Neural Networks ensembles through Single-Iteration Optimization, a fast optimization composed of only one iteration that is no more effective than a random search. Essentially, the idea is to provide the same capability offered by long-term optimizations, however, without their computational loads. The results among four well-known literature datasets revealed that creating one-iteration optimized ensembles provide promising results while diminishing the time to achieve them.

A Fast and Accurate Approach to Multiple-Vehicle Localization and Tracking from Monocular Aerial Images

In this work we present a novel end-to-end solution for tracking objects (i.e., vessels), using video streams from aerial drones, in dynamic maritime environments. Our method relies on deep features, which are learned using realistic simulation data, for robust object detection, segmentation and tracking. Furthermore, we propose the use of rotated bounding-box representations, which are computed by taking advantage of pixel-level object segmentation, for improved tracking accuracy, by reducing erroneous data associations during tracking, when combined with the appearance-based features. A thorough set of experiments and results obtained in a realistic shipyard simulation environment, demonstrate that our method can accurately, and fast detect and track dynamic objects seen from a top-view.

Object segmentation in cluttered environment based on gaze tracing and gaze blinking

People with disabilities, such as patients with motor paralysis conditions, lack independence and cannot move most parts of their bodies except for their eyes. Supportive robot technology is highly beneficial in supporting these types of patients. We propose a gaze-informed location-based (or gaze-based) object segmentation, which is a core module of successful patient-robot interaction in an object-search task (i.e., a situation when a robot has to search for and deliver a target object to the patient). We have introduced the concepts of gaze tracing (GT) and gaze blinking (GB), which are integrated into our proposed object segmentation technique, to yield the benefit of an accurate visual segmentation of unknown objects in a complex scene. Gaze tracing information can be used as a clue as to where the target object is located in a scene. Then, gaze blinking can be used to confirm the position of the target object. The effectiveness of our proposed method has been demonstrated using a humanoid robot in experiments with different types of highly cluttered scenes. Based on the limited gaze guidance from the user, we achieved an 85% F-score of unknown object segmentation in an unknown environment.