IEEE Trans Image Process. 2017 Oct;26(10):4832-4842. doi: 10.1109/TIP.2017.2699791. Epub 2017 Apr 28.
Although visual object tracking algorithms are capable of handling various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking by detection method, the key issue lies in detecting the target over the whole frame and updating systematically a target model based on the last detected appearance to avoid the drift phenomenon. This paper aims at proposing a novel robust tracking algorithm by fusing the frame level detection strategy of tracking, learning, & detection with the systematic model update strategy of Kernelized Correlation Filter tracker. The risk of drift is mitigated by the fact that the model updates are primarily driven by the detections that occur in the spatial neighborhood of the latest detections. The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two state-of-the-art tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short ends by virtue of other. Extensive evaluation of the proposed method based on different metrics is carried out on the data sets ALOV300++, Visual Tracker Benchmark, and Visual Object Tracking. We demonstrated its performance in terms of robustness and success rate by comparing with state-of-the-art trackers.
虽然视觉目标跟踪算法能够单独处理各种具有挑战性的场景，但没有一种算法强大到足以同时处理所有挑战。对于任何基于检测的在线跟踪方法，关键问题在于在整个帧中检测目标，并根据最后一次检测到的外观系统地更新目标模型，以避免漂移现象。本文旨在提出一种新的鲁棒跟踪算法，通过融合跟踪、学习和检测的帧级检测策略以及核相关滤波器跟踪器的系统模型更新策略。由于模型更新主要由最新检测的空间邻域内的检测驱动，因此降低了漂移的风险。选择跟踪器的动机是它们在处理跟踪挑战方面具有互补性。所提出的算法有效地将两种基于保守对应度量的最先进的跟踪算法与战略模型更新相结合，利用了两者的优势，并通过其他方面的优势弥补了它们的不足。基于不同的度量标准，在 ALOV300++、视觉跟踪基准和视觉目标跟踪数据集上对所提出的方法进行了广泛评估。我们通过与最先进的跟踪器进行比较，展示了其在鲁棒性和成功率方面的性能。
