Data association based ant tracking with interactive error correction

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI:10.1109/WACV.2014.6836003

Hoan Nguyen, Thomas Fasciano, D. Charbonneau, A. Dornhaus, M. Shin

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引用次数: 6

Abstract

The tracking of ants in video is important for the analysis of their complex group behavior. However, the manual analysis of these videos is tedious and time consuming. Automated tracking methods tend to drift due to frequent occlusions during their interactions and similarity in appearance. Semi-automated tracking methods enable corrections of tracking errors by incorporating user interaction. Although it is much lower than manual analysis, the required user time of the existing method is still typically 23 times the actual video length. In this paper, we propose a new semi-automated method that achieves similar accuracy while reducing the user interaction time by (1) mitigating user wait time by incorporating a data association tracking method to separate the tracking from user correction, and (2) minimizing the number of candidates visualized for user during correction. This proposed method is able to reduce the user interaction time by 67% while maintaining the accuracy within 3% of the previous semi-automated method [11].

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基于数据关联的交互式纠错蚁群跟踪

视频中蚂蚁的跟踪对分析蚂蚁复杂的群体行为具有重要意义。然而，手工分析这些视频既繁琐又耗时。由于相互作用过程中频繁的咬合和外观上的相似性，自动跟踪方法容易产生漂移。半自动跟踪方法可以通过合并用户交互来纠正跟踪错误。虽然比人工分析低得多，但现有方法所需的用户时间通常仍然是实际视频长度的23倍。在本文中，我们提出了一种新的半自动化方法，在实现类似精度的同时，通过以下方式减少用户交互时间:(1)通过合并数据关联跟踪方法来减少用户等待时间，从而将跟踪与用户更正分开;(2)在更正期间最小化为用户可视化的候选数量。该方法能够将用户交互时间减少67%，同时将精度保持在先前半自动方法[11]的3%以内。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision

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