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摘要: 提出了一个在单个固定摄像机下进行多目标跟踪的方法.利用亮度和色度混合模型和卡尔曼滤波器来检测跟踪目标,为了利于预测和解释被遮挡的物体,建立了场景的模型.在遮挡的情况下,和传统的盲跟踪不同,本文中的目标状态是由可用的部分观测来估计的.对目标的观测取决于预测、前景观测和场景模型.这使得本文算法在定性或定量的分析下都表现出更加鲁棒的性能.Abstract: This paper presents a framework for multi-object tracking from a single fixed camera. The potential objects to track are detected with. intensity-plus-chromaticity mixture models. The region-based representations of each object are tracked and predicted using a Kalman filter. A scene model is created to help predict and interpret the occluded or exiting objects. Unlike the traditional blind tracking during occlusion, the object states are estimated using partial observations whenever available. The observability of each object depends on the predictive measurement of the object, the foreground region measurement, and the scene model. This makes the algorithm more robust in terms of both qualitative and quantitative criteria.
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Key words:
- Partial observation /
- scene model /
- foreground region
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