Object tracking is one of the most fundamental and important fields in computer vision with a wide range of applications. Although great progress has been made in object tracking combined with detection, there is still enormous challenges in real-time applications and for the computer cannot effectively capture the temporal correlations of targets and background clutter. In order to improve the performance of tracking algorithms under complex unconstrained conditions, we propose a novel tracking framework based on adaptive detection, called adaptive detection tracking (ADT). First, we exploit the temporal correlation of the recurrent neural network to predict the target’s motion direction and efficiently update the region of interest (RoI) in the narrow range of the next frame. Then, the algorithm utilizes the correlation filter to initialize the defined region of interest based on the threshold. If the Interaction of Union (IoU) of the predicted bounding box and the groundtruth bounding box is greater than the set threshold, the predicted bounding box will be directly output as the tracking results, whereas the detection is adaptively carried out in the determined RoI. Finally, the predicted bounding box refines the direction model as the input of the next frame to complete the whole tracking flow. Our proposed adaptive detection tracking mechanism can efficiently realize non-frame-by-frame adaptive detection with excellent tracking accuracy and is more robust in the unconstrained scenes, especially for occlusion. Comprehensive experiments demonstrate that our approach consistently achieves state-of-the-art results and runs in real-time on six large tracking benchmarks, including OTB100, VOT2016, VOT2017, TC128, UAV123 and LaSOT datasets.

中文翻译：

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ADT：基于自适应检测的对象跟踪算法

目标跟踪是计算机视觉中最基本和最重要的领域之一，具有广泛的应用。尽管在目标跟踪与检测相结合方面取得了很大进展，但实时应用仍然存在巨大挑战，计算机无法有效捕捉目标和背景杂波的时间相关性。为了提高复杂无约束条件下跟踪算法的性能，我们提出了一种基于自适应检测的新型跟踪框架，称为自适应检测跟踪（ADT）。首先，我们利用循环神经网络的时间相关性来预测目标的运动方向，并在下一帧的狭窄范围内有效地更新感兴趣区域（RoI）。然后，该算法利用相关滤波器根据阈值初始化定义的感兴趣区域。如果预测边界框与真实边界框的联合交互（IoU）大于设置的阈值，则预测边界框将直接作为跟踪结果输出，而检测则在确定的 RoI 中自适应地进行。最后，预测的边界框细化方向模型作为下一帧的输入完成整个跟踪流程。我们提出的自适应检测跟踪机制可以有效地实现非逐帧自适应检测，具有出色的跟踪精度，并且在无约束场景中更加鲁棒，特别是对于遮挡。