论文标题
使用上下文功能进行动作解析
Action parsing using context features
论文作者
论文摘要
我们提出了一种解析算法的动作，以解析一个视频序列，其中包含未知数量的动作在其动作段中。我们认为，上下文信息，尤其是视频顺序中其他动作的时间信息，对于动作分割很有价值。所提出的解析算法会暂时将视频序列段分为动作段。使用动态编程搜索算法发现最佳的时间分割，该算法优化了整体分类置信度评分。使用从该段计算得出的局部特征以及根据序列的其他候选动作段计算得出的上下文特征确定每个段的分类评分。早餐活动数据集的实验结果表明，与现有的最新解析技术相比，分割精度的提高。
We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into action segments. The optimal temporal segmentation is found using a dynamic programming search algorithm that optimizes the overall classification confidence score. The classification score of each segment is determined using local features calculated from that segment as well as context features calculated from other candidate action segments of the sequence. Experimental results on the Breakfast activity data-set showed improved segmentation accuracy compared to existing state-of-the-art parsing techniques.
