抗遮挡目标跟踪的模型学习综述

视觉目标跟踪任务中的遮挡问题是最具挑战的场景属性之一,研究有效的抗遮挡模型学习方案,对构建适应复杂场景的长期鲁棒跟踪模型具有重要意义.剖析了遮挡影响跟踪性能的本质原因,以抗遮挡性能较好的先进跟踪算法为研究对象,系统分析了模型学习中有效抗遮挡机制,并对其改善长短期遮挡问题的有效性进行比较分析,包括以硬负样本挖掘、有效样本...

Survey of Model Learning for Anti-occlusion Object Tracking

The occlusion problem in the visual object tracking task is one of the most challenging scene attributes. The study of effective anti-occlusion model learning schemes is of great significance for building long-term robust tracking models that adapt to complex scenes. First, it analyzes essentially why occlusion affects the tracking performance, and then advanced tracking algorithms with better anti-occlusion performance are taken as the research object, it systematically analyzes the effective anti-occlusion mechanism in model learning, and compares its effectiveness in improving long-term and short-term occlusion problems. The analysis includes training sample quality improvement strategies to provide sufficient discriminative information for the model that contains hard negative sample mining, effective sample management, and occlusion of hard positive samples generation; Passive and stable learning methods based on time consistency learning, adaptive appearance learning, and multiple active learning strategies suitable for tracking tasks builds robust models that can resist scene interference, target deformation and other factors, such as domain attributes, target perception, interference perception, feature fusion, etc.; The update strategy balances the adaptability and stability of the model tracking object with change state online such as manual confidence evaluation, adaptive decision-making, and time series memory storage, adaptive estimation template. Then, by comparing the performance of the representative tracking algorithm in occlusion and background clutter, out of view, in and out of the plane rotation, and deformation, the effectiveness of each strategy against occlusion is analyzed in detail, and it is pointed out that compared to the update strategy, the data processing and learning strategy design is more helpful to improve the anti-occlusion performance; Meanwhile, the applicability of each strategy to long-term occlusion, background clutter, out-of-view, and other attributes is analyzed, as well as the strategies applicable to multiple types of complex scenes. Finally, the effective anti-occlusion strategies are summarized, and research direction of backbone network replacement and migration scene understanding, motion law, and other prior information to the tracking task is proposed.