Visual Object Tracking by Moving Horizon Estimation with Probabilistic Data Association
Visual Object Tracking by Moving Horizon Estimation with Probabilistic Data Association
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DOI：
10.1109/sii46433.2020.9026198
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Tomoya Kikuchi;K. Nonaka;K. Sekiguchi
中科院分区：
文献类型：
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作者：
Tomoya Kikuchi;K. Nonaka;K. Sekiguchi
视觉传感器不仅广泛用于检测和识别，而且还用于测量运动物体的姿态。但在拥挤的环境中，遮挡往往会干扰测量，并且由于误识别而导致的错误数据关联会降低跟踪性能。概率数据关联滤波器（PDAF）被认为是有用的，以解决这些问题，其中观察到的特征是加权的概率，以处理多个观察，以及科普遮挡和错误识别。提出了一种新的目标跟踪方法，将PDAF引入到滚动时域估计（MHE）框架中，以处理多帧跟踪和物理约束。通过与PDAF算法的比较，对所提方法的性能进行了评估。
Vision sensors are widely used not only for detection and recognition, but also measurement of the pose of the moving objects. But in the crowded environment, occlusion often disrupts the measurement, and wrong data association due to misrecognition deteriorates the tracking performance. Probabilistic data association filter (PDAF) is known as useful to address such issues, in which observed features are weighted by probability to deal with multiple observations as well as to cope with occlusion and false recognition. This paper presents a novel object tracking method in which PDAF is incorporated into moving horizon estimation (MHE) framework to deal with multiple frame tracking and physical constraints. The performance of the proposed method is evaluated by comparing with the PDAF.