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首页> 外文期刊>Multimedia, IEEE Transactions on >Mean-Shift and Sparse Sampling-Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking
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Mean-Shift and Sparse Sampling-Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking

机译：基于均值漂移和稀疏采样的SMC-PHD滤波用于音频可视扬声器跟踪

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摘要

The probability hypothesis density (PHD) filter based on sequential Monte Carlo (SMC) approximation (also known as SMC-PHD filter) has proven to be a promising algorithm for multispeaker tracking. However, it has a heavy computational cost as surviving, spawned, and born particles need to be distributed in each frame to model the state of the speakers and to estimate jointly the variable number of speakers with their states. In particular, the computational cost is mostly caused by the born particles as they need to be propagated over the entire image in every frame to detect the new speaker presence in the view of the visual tracker. In this paper, we propose to use the audio data to improve the visual SMC-PHD (V-SMC-PHD) filter by using the direction of arrival angles of the audio sources to determine when to propagate the born particles and reallocate the surviving and spawned particles. The tracking accuracy of the audio-visual SMC-PHD (AV-SMC-PHD) algorithm is further improved by using a modified mean-shift algorithm to search and climb density gradients iteratively to find the peak of the probability distribution, and the extra computational complexity introduced by mean-shift is controlled with a sparse sampling technique. These improved algorithms, named as AVMS-SMC-PHD and sparse-AVMS-SMC-PHD, respectively, are compared systematically with AV-SMC-PHD and V-SMC-PHD based on the AV16.3, AMI, and CLEAR datasets.
机译：基于顺序蒙特卡洛（SMC）近似的概率假设密度（PHD）滤波器（也称为SMC-PHD滤波器）已被证明是用于多扬声器跟踪的一种有前途的算法。但是，这需要很大的计算成本，因为需要在每个帧中分配生存，产生和生成的粒子以对说话者的状态进行建模，并共同估计说话者及其状态的可变数量。特别是，计算成本主要由出生粒子引起，因为它们需要在每一帧中传播到整个图像上才能在视觉跟踪器的视图中检测到新说话者的存在。在本文中，我们建议通过使用音频数据来改进视觉SMC-PHD（V-SMC-PHD）滤波器，方法是通过使用音频源的到达角方向来确定何时传播出生粒子并重新分配幸存的粒子。产生的粒子。通过使用改进的均值漂移算法迭代搜索和爬升密度梯度以找到概率分布的峰值，并进行额外的计算，进一步提高了视听SMC-PHD（AV-SMC-PHD）算法的跟踪精度均值漂移引入的复杂性是通过稀疏采样技术来控制的。根据AV16.3，AMI和CLEAR数据集，将这些改进的算法分别命名为AVMS-SMC-PHD和稀疏AVMS-SMC-PHD，分别与AV-SMC-PHD和V-SMC-PHD进行比较。

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