基于深度学习的目标视频跟踪算法综述

深度学习理论在计算机视觉中的应用日趋广泛，在目标分类、检测领域取得了令人瞩目的成果，但是深度学习理论在目标跟踪领域的早期应用中，由于存在跟踪时只有目标为正样本，缺乏数据支持，对位置信息依赖程度高等问题，因而应用效果并不理想，传统方法仍占据主流地位.近年来，随着技术的不断发展，深度学习在目标跟踪方向取得了长足的进步.本文首先介绍了目标跟踪技术的基本概念和主要方法，然后针对深度学习在目标跟踪领域的发展现状，从基于深度特征的目标跟踪和基于深度网络的目标跟踪两方面重点阐述了深度学习在该领域的应用方法，并对近期较为流行的基于孪生网络的目标跟踪进行了详细介绍.最后对近年来深度学习在目标跟踪领域取得的成果，以及未来的发展方向作了总结和展望.

Survey on Video Object Tracking Algorithms Based on Deep Learning

Deep learning has achieved remarkable results in target detection and classification when applied to computer vision. But in the field of object tracking, the target is only considered as a positive sample. Being lack of data support and more dependent on the location information, deep learning did not achieve remarkable effect in the object tracking field, while the traditional methods still occupy the main position. However, with the development of technology, deep learning has progressed greatly in the direction of object tracking in recent years. This paper introduces the basic concept and the main methods of target tracking technology. Combined with the development of deep learning in recent years in the field of target tracking, the emphasis is on the basic approach of target tracking technology with tracking by deep feature and tracking based on deep network and introduces the recently popular target tracking based on Siamese network in detail. At the end, the achievements of deep learning in the field of target tracking in recent years and future development of object tracking are summarized and prospected.