目标物智能跟踪与分割融合算法及其在变电站视频监控中的应用

A Unified Algorithm for Object Tracking and Segmentation and its Application on Intelligent Video Surveillance for Transformer Substation

针对变电站智能视频监控的多样化需求,提出一种基于孪生网络Siam RPN框架的目标跟踪与分割的融合算法,可以同时完成人员、工程车辆、入侵异物等多种目标的跟踪与分割,准确度高且实时性好。该算法在SiamRPN算法基础上,设计高效的分割分支用于得到跟踪目标的精细掩膜,并且提出一种掩膜质量评判方法,利用分割的结果进一步提升目标跟踪的精度。此外,该文采用在线模板更新的策略,使得该算法对于长视频更具鲁棒性。该算法不仅在变电站的视频监控中表现出了很高的准确率和鲁棒性,而且在目标跟踪最权威的数据集VOT2018上取得较好的分析结果。

Considering the diversified requirements in intelligent video surveillance for transformer substation, a unified algorithm for object tracking and segmentation was proposed which is able to track and segment humans, vehicles, and many other foreign objects with real-time speed. Based on SiamRPN, an efficient segmentation branch was designed to get high quality mask for target object. In addition, in order to enhance tracking accuracy with the result of segmentation, a mask quality scoring method was proposed. Besides, the proposed method adopts templet updating strategy, which makes the method more robust for long sequences. This algorithm not only reaches high accuracy and robustness in the practical task of video surveillance for transformer substation, but also gets high performance on VOT2018 benchmark.