Fine-grained motion recognition is most important for such video retrieval, and most work nowadays focuses on coarse-grained and fine-grained actions in motion recognition, without being involved in many uses. To solve this problem, in this system, it have a dataset that challenged a basketball game by annotating detailed actions in a video. Adaptive Multi-Label Classification methods for basketball action recognition benchmark also provides data about the system. In addition, this method is proposed to integrate the FPGA into a network of two data streams in order to find the finest areas of basketball action recognition and extracts the features of the recognition system. This proposed system gives significantly a better and superior results than the existing methods. Taken individually, the surrounding first-person footage can be associated with similar situations in the past and compared with the visual semantics of the spatial and social layout of personal records. In general, first-person videos can track common interests, and can be linked to group of individuals in this system.

细粒度的运动识别对于这种视频检索最为重要，并且当今的大多数工作都集中在运动识别中的粗粒度和细粒度动作上，而没有涉及很多用途。为了解决这个问题，在该系统中，它具有通过注释视频中的详细动作来挑战篮球比赛的数据集。自适应多标签分类篮球动作识别基准测试方法还提供有关系统的数据。另外，该方法被提议将FPGA集成到两个数据流的网络中，以便找到篮球动作识别的最佳区域并提取识别系统的特征。与现有方法相比，该提议的系统明显提供了更好和更好的结果。单独拍摄时，周围的第一人称镜头可以与过去的类似情况相关联，并且可以与个人记录的空间和社会布局的视觉语义进行比较。通常，第一人称视频可以跟踪共同的兴趣，并且可以链接到该系统中的个人组。