Key-Point Sequence Lossless Compression for Intelligent Video Analysis
This work addresses efficient feature coding for urban computing applications, representing an incremental improvement in video analysis.
The paper tackles the problem of compressing key-point sequences for intelligent video analysis by proposing a lossless compression approach that eliminates spatial and temporal redundancies, achieving validated effectiveness on four types of key-point sequences.
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.