论文标题
可视跟踪的深度卷积似然粒子滤波器
Deep Convolutional Likelihood Particle Filter for Visual Tracking
论文作者
论文摘要
我们提出了一种用于卷积相关视觉跟踪器的新型粒子滤波器。我们的方法使用相关响应图来估计似然分布，并将这些可能性用作样品颗粒的建议密度。基于目标过渡分布的建议分布比提案密度更可靠，因为相关响应图提供了有关目标位置的其他信息。此外，我们的粒子过滤器还搜索了可能性分布中的多种模式，从而改善了目标遮挡场景的性能，同时通过更有效地采样粒子来降低计算成本。在其他具有挑战性的场景中，例如涉及运动模糊的情况，只有一种模式，但可能需要更大的搜索区域，我们的粒子过滤器允许可能性分布的差异增加。我们在Visual Tracker基准V1.1（OTB100）上测试了算法，我们的实验结果表明，我们的框架的表现优于最先进的方法。
We propose a novel particle filter for convolutional-correlation visual trackers. Our method uses correlation response maps to estimate likelihood distributions and employs these likelihoods as proposal densities to sample particles. Likelihood distributions are more reliable than proposal densities based on target transition distributions because correlation response maps provide additional information regarding the target's location. Additionally, our particle filter searches for multiple modes in the likelihood distribution, which improves performance in target occlusion scenarios while decreasing computational costs by more efficiently sampling particles. In other challenging scenarios such as those involving motion blur, where only one mode is present but a larger search area may be necessary, our particle filter allows for the variance of the likelihood distribution to increase. We tested our algorithm on the Visual Tracker Benchmark v1.1 (OTB100) and our experimental results demonstrate that our framework outperforms state-of-the-art methods.
