Extracting retrievable features from video is of great significance for
structured video database construction, video copyright protection and fake
video rumor refutation. Inspired by point cloud data processing, this paper
proposes a method for marking anything (MA) in the video, which can extract the
contour features of any target in the video and convert it into a feature
vector with a length of 256 that can be retrieved. The algorithm uses YOLO-v8
algorithm, multi-object tracking algorithm and PointNet++ to extract contour of
the video detection target to form spatial point cloud data. Then extract the
point cloud feature vector and use it as the retrievable feature of the video
detection target. In order to verify the effectiveness and robustness of
contour feature, some datasets are crawled from Dou Yin and Kinetics-700
dataset as experimental data. For Dou Yin's homogenized videos, the proposed
contour features achieve retrieval accuracy higher than 97% in Top1 return
mode. For videos from Kinetics 700, the contour feature also showed good
robustness for partial clip mode video tracing.

本文提出了一种基于点云数据处理的标记任意视频目标的方法，可以提取视频检测目标的轮廓特征，转化为长度为 256 的可检索特征向量，通过算法提取该特征向量，用作视频检测目标的可检索特征。

点云在提取视频目标特征中的应用

Marking anything: application of point cloud in extracting video target  features

The number of videos being produced and consequently stored in databases for
video streaming platforms has been increasing exponentially over time. This
vast database should be easily index-able to find the requisite clip or video
to match the given search specification, preferably in the form of a textual
query. This work aims to provide an end-to-end pipeline to search a video
database with a voice query from the end user. The pipeline makes use of
Recurrent Neural Networks in combination with Convolutional Neural Networks to
generate captions of the video clips present in the database.

本文介绍了一种使用循环神经网络和卷积神经网络生成视频片段标题的方法，以便用户能够使用语音查询从视频数据库中搜索匹配给定查询规范的需要的视频片段。