Variational Bayesian tracking: Whole track convergence for large-scale ecological video monitoring
Variational Bayesian tracking: Whole track convergence for large-scale ecological video monitoring
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DOI：
10.1109/ijcnn.2013.6707130
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
J. Christmas;R. Everson;Rolando Rodríguez-Muñoz;T. Tregenza
中科院分区：
文献类型：
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作者：
J. Christmas;R. Everson;Rolando Rodríguez-Muñoz;T. Tregenza
变分贝叶斯近似为贝叶斯推理提供了一种计算速度快的数值近似方法。我们研究变分贝叶斯方法过滤和平滑连续隐马尔可夫模型，特别是那些尖锐的峰值，非线性观测密度。我们表明，通过以正确的顺序进行变分更新，可以实现跟踪状态的鲁棒收敛。我们应用整个跟踪收敛算法跟踪野生蟋蟀在视频流中，并描述了如何动物可能被识别出他们的轨道的特点。我们还展示了如何识别字母数字标签可以在光线不足的条件下读取。
Variational Bayesian approximations offer a computationally fast alternative to numerical approximations for Bayesian inference. We examine variational Bayesian methods for filtering and smoothing continuous hidden Markov models, in particular those with sharply-peaked, nonlinear observations densities. We show that, by making variational updates in the correct order, robust convergence to the tracked state may be achieved. We apply the whole track convergence algorithm to tracking wild crickets in video streams and describe how animals may be identified from the characteristics of their tracks. We also show how identifying alphanumeric tags may be read under poor lighting conditions.