Conditional Convolutions for End-to-End Single-Stage Video Text Detection

Abstract

We propose a simple yet effective single-stage video text detection framework, termed CVTD (Conditional convolutions for Video Text Detection), which, to the best of our knowledge, is the first end-to-end single-stage video text detection framework.Most existing video text detection methods adopt text tracking to enhance text detection performance, but treat text detection and tracking as two separate tasks. In contrast, we propose to solve text detection and tracking in an end-to-end, unified way. Instead of using an additional tracking module, we employ dynamic instance-aware conditional convolution (CondConv) to implicitly model the temporal variation of video text. Each CondConv represents a text instance over time and is responsible to predict corresponding text mask. It is propagated and updated frame-by-frame to perform text detection and tracking in a seamless manner.CVTD enjoys two advantages: (1) Text detection and tracking are integrated in a single-stage network, eliminating the need for additional text tracking process. (2) The CondConv transferred across frames can compactly encodes temporal context features of text, leading to enhanced text detection performance and real-time inference speed. Experiments on multiple video text benchmarks demonstrate the superiority of our method in terms of both accuracy and efficiency.