School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
PLoS One. 2023 Jan 6;18(1):e0279240. doi: 10.1371/journal.pone.0279240. eCollection 2023.
Discriminative correlation filter (DCF) tracking algorithms are commonly used for visual tracking. However, we observed that different spatio-temporal targets exhibit varied visual appearances, and most DCF-based trackers neglect to exploit this spatio-temporal information during the tracking process. To address the above-mentioned issues, we propose a three-way adaptive spatio-temporal correlation filtering tracker, named ASCF, that makes fuller use of the spatio-temporal information during tracking. To be specific, we extract rich local and global visual features based on the Conformer network, establish three correlation filters at different spatio-temporal locations during the tracking process, and the three correlation filters independently track the target. Then, to adaptively select the correlation filter to achieve target tracking, we employ the average peak-to-correlation energy (APCE) and the peak-to-sidelobe ratio (PSR) to measure the reliability of the tracking results. In addition, we propose an adaptive model update strategy that adjusts the update frequency of the three correlation filters in different ways to avoid model drift due to the introduction of similar objects or background noise. Extensive experimental results on five benchmarks demonstrate that our algorithm achieves excellent performance compared to state-of-the-art trackers.
判别相关滤波（DCF）跟踪算法常用于视觉跟踪。然而，我们观察到不同的时空目标表现出不同的视觉外观，而大多数基于 DCF 的跟踪器在跟踪过程中忽略了利用这种时空信息。为了解决上述问题，我们提出了一种三向自适应时空相关滤波跟踪器，名为 ASCF，它在跟踪过程中更充分地利用了时空信息。具体来说，我们基于 Conformer 网络提取丰富的局部和全局视觉特征，在跟踪过程中建立三个不同时空位置的相关滤波器，三个相关滤波器独立跟踪目标。然后，为了自适应地选择相关滤波器来实现目标跟踪，我们采用平均峰值相关能量（APCE）和峰值旁瓣比（PSR）来衡量跟踪结果的可靠性。此外，我们提出了一种自适应模型更新策略，以不同的方式调整三个相关滤波器的更新频率，以避免由于引入相似物体或背景噪声而导致的模型漂移。在五个基准上的广泛实验结果表明，与最先进的跟踪器相比，我们的算法取得了优异的性能。
