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.

细粒度的动作识别对于此类视频检索来说是最重要的，目前大多数工作都集中在动作识别中的粗粒度和细粒度动作，而没有涉及很多用途。为了解决这个问题，在这个系统中，它有一个数据集，通过注释视频中的详细动作来挑战篮球比赛。用于篮球动作识别基准的自适应多标签分类方法还提供了有关该系统的数据。此外，该方法还提出将FPGA集成到两个数据流的网络中，以找到篮球动作识别的最佳区域并提取识别系统的特征。该系统提供了比现有方法明显更好的结果。单独来看，周围的第一人称镜头可以与过去的类似情况相关联，并与个人记录的空间和社会布局的视觉语义进行比较。一般来说，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体组。