To improve the performance of video compression for machine vision analysis tasks, a video coding for machines (VCM) standard working group was established to promote standardization procedures. In this paper, recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases, requirements, evaluation frameworks and corresponding metrics of the VCM standard are given. Then the existing methods are presented, introducing the existing proposals by category and the research progress of the latest VCM conference. Finally, we give conclusions.

Segmentation-based scene text detection has drawn a great deal of attention, as it can describe the text instance with arbitrary shapes based on its pixel-level prediction. However, most segmentation-based methods suffer from complex post-processing to separate the text instances which are close to each other, resulting in considerable time consumption during the inference procedure. A label enhancement method is proposed to construct two kinds of training labels for segmentation-based scene text detection in this paper. The label distribution learning (LDL) method is used to overcome the problem brought by pure shrunk text labels that might result in sub-optimal detection performance. The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance without sacrificing inference speed.