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Target state recognition of basketball players based on video image detection and FPGA

机译：基于视频图像检测和FPGA的篮球运动员目标国家识别

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摘要

In the optimization for target tracking of basketball players, the traditional approach has a major drawback. The function results in a highly non-convex function is defined as a linear combination of spatial and appearance constraints on the target. Very dense graph has been configured to capture the attributes of the target. Video detection is based on sports player game movement, then edge detection based on video running. They were moving the ball position for each player. FPGA configuration is used for edge detection algorithm is fully supported from playbacks. If the system is verified, it shows a video image is an edge detecting system that can detect a highly accurate edge. Learning techniques for the extraction of deep foundation, started by the main features, and significant progress has been made in this respect. The latest progress uses various learning techniques of in-depth and detailed investigation in target detection results. Several topics are covered, including gradient direction, histogram, a single detector and a double shot, datasets, indicators, acceleration, and the current state of art probe. Some important applications in the object detection region include a detailed discussion of the detection and detection of a pedestrian population.
机译：在篮球运动员的目标跟踪优化中，传统方法具有重要的缺点。该功能导致高度非凸起函数被定义为目标上的空间和外观约束的线性组合。非常密集的图形已配置为捕获目标的属性。视频检测基于运动播放器游戏运动，然后基于视频运行的边缘检测。他们正在为每个球员移动球位。 FPGA配置用于边缘检测算法从播放完全支持。如果验证系统，则显示视频图像是可以检测高精度边缘的边缘检测系统。深基础提取的学习技术，由主要特征开始，在这方面取得了重大进展。最新进展利用目标检测结果中的深入和详细调查的各种学习技术。覆盖了几个主题，包括梯度方向，直方图，单个探测器和双射击，数据集，指示器，加速度和最新状态。物体检测区域中的一些重要应用包括对人口的检测和检测的详细讨论。

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