FastClip: An Efficient Video Understanding System with Heterogeneous Computing and Coarse-to-fine Processing

Liming Zhao, Siyang Sun, Yanhao Zhang, Yun Zheng, Pan Pan

Abstract: Recently, video medias are exponentially growing in many areas such as E-commerce shopping and gaming. Understanding the video contents is critical for real-world applications. However, processing long videos is usually time-consuming and expensive. In this paper, we present an efficient video understanding system, which aims to speed up the video processing with a coarse-to-fine two-stage pipeline and heterogeneous computing framework. First, we use a coarse but fast multi-modal filtering module to recognize and remove useless video segments from a long video, which could be deployed on an edge device and reduce computations for the next processing. Second, several semantic models are applied for finely parsing the remained sequences. To accelerate the model inference, we propose a novel heterogeneous computing framework, which trains a model with lightweight and heavyweight backbones to support a distributed deployment on a powerful device (e.g., cloud or GPU) and another different device (e.g., edge or CPU). In this way, the model could be both efficient and effective. The proposed system has been widely used in Alibaba, including “Taobao Live Analysis” and “Commodity Short-Video Generation”, which could achieve a 10 × speedup for the system.