基于桥接分布的贝叶斯目标跟踪意图预测
Bayesian Intent Prediction in Object Tracking Using Bridging Distributions
摘要：
在几个应用领域，如人机交互，监视和防御，确定跟踪对象的意图使系统能够帮助用户/操作员，并促进有效的，可能是自动化的决策。在本文中，我们提出了一种概率推理方法，允许预测，提前跟踪对象的预定目的地及其未来的轨迹。在这里介绍的框架内，所观察到的对象的部分轨道被建模为在其目的地终止的马尔可夫桥的一部分，因为目标路径，虽然随机，必须在预期的端点结束。这捕获轨迹中的潜在长期依赖性，如对象意图所指示的。通过确定部分轨道从特定建造的桥梁引出的可能性，评估多个可能目的地中的每一个的概率。这些桥还可以用于产生潜在系统状态（例如，对象位置、速度等）的精细估计，预测其未来值（直到到达指定端点）并估计到达时间。这表明，导致一个低复杂度的卡尔曼滤波器为基础的实现的推理例程，其中任何线性高斯运动模型，包括目的地恢复的，可以应用。自由的手指向手势的数据收集在一个仪表车辆和合成轨迹的船只走向多个可能的港口被用来证明所提出的方法的有效性。
英文摘要：
In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g. object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading towards multiple possible harbours are utilised to demonstrate the effectiveness of the proposed approach.
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