In this paper, we address the problem of online multi-object tracking based on the Maximum a Posteriori (MAP) framework. Given the observations up to the current frame, we estimate the optimal object trajectories via two MAP estimation stages: object detection and data association. By introducing the sequential trajectory prior, i.e., the prior information from previous frames about “good” trajectories, into the two MAP stages, the inference of optimal detections is refined and the association correctness between trajectories and detections is enhanced. Furthermore, the sequential trajectory prior allows the two MAP stages to interact with each other in a sequential manner, which jointly optimizes the detections and trajectories to facilitate online multi-object tracking. Compared with existing methods, our approach is able to alleviate the association ambiguity caused by noisy detections and frequent inter-object interactions without using sophisticated association likelihood models. The experiments on publicly available challenging datasets demonstrate that our approach provides superior tracking performance over state-of-the-art algorithms in various complex scenes.

在本文中，我们解决了基于最大后验（MAP）框架的在线多对象跟踪问题。给定当前帧的观测值，我们通过两个MAP估计阶段估计最佳对象轨迹：对象检测和数据关联。通过引入先验的顺序轨迹，即将来自先前帧的有关“良好”轨迹的先验信息分为两个MAP阶段，优化了最优检测的推理，并增强了轨迹与检测之间的关联正确性。此外，顺序轨迹先验允许两个MAP阶段以顺序方式相互交互，从而共同优化检测和轨迹，以方便在线多对象跟踪。与现有方法相比，我们的方法无需使用复杂的关联似然模型，就能够缓解由于噪声检测和频繁的对象间交互作用而导致的关联模糊性。在公开可用的具有挑战性的数据集上进行的实验表明，在各种复杂场景中，我们的方法提供了优于最新算法的出色跟踪性能。