论文标题
智能视频分析的关键点序列无损压缩
Key-Point Sequence Lossless Compression for Intelligent Video Analysis
论文作者
论文摘要
最近已经考虑使用功能编码来促进城市计算的智能视频分析。前端中提取的功能不是原始视频，而是将其编码并传输到后端以进行进一步处理。在本文中，我们提出了一种无损密钥序列压缩方法，用于有效的特征编码。这种预测和编码策略的本质是消除视频中关键点的空间和时间冗余。提出了具有自适应模式选择方法的多个预测模式，以处理具有各种结构和运动的关键点序列。实验结果验证了在视频分析中，提出的方案对四种广泛使用的关键点序列的有效性。
Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate the spatial and temporal redundancies of key points in videos. Multiple prediction modes with an adaptive mode selection method are proposed to handle key-point sequences with various structures and motion. Experimental results validate the effectiveness of the proposed scheme on four types of widely used key-point sequences in video analysis.
