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摘要: 视频目标跟踪是计算机视觉的重要研究课题, 在视频监控、机器人、人机交互等方面具有广泛应用. 大数据时代的到来及深度学习方法的出现, 为视频目标跟踪的研究提供了新的契机. 本文首先阐述了视频目标跟踪的基本研究框架. 对新时期视频目标跟踪研究的特点与趋势进行了分析, 介绍了国际上新兴的数据平台、评测方法. 重点介绍了目前发展迅猛的深度学习方法, 包括堆叠自编码器、卷积神经网络等在视频目标跟踪中的最新具体应用情况并进行了深入分析与总结. 最后对深度学习方法在视频目标跟踪中的未来应用与发展方向进行了展望.Abstract: Video object tracking is an important research topic of computer vision with numerous applications including surveillance, robotics, human-computer interface, etc. The coming of big data era and the rise of deep learning methods have offered new opportunities for the research of tracking. Firstly, we present the general framework for video object tracking research. Then, we introduce new arisen datasets and evaluation methodology. We highlight the application of the rapid-developing deep-learning methods including stacked autoencoder and convolutional neural network on video object tracking. Finally, we have a discussion and provide insights for future.
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Key words:
- Object tracking /
- video analysis /
- online learning /
- deep learning /
- big data
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