An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features
An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features
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DOI：
10.1109/lsp.2014.2364458
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发表时间：
2015-05-01
影响因子：
3.9
通讯作者：
Robertson, Neil M.
中科院分区：
文献类型：
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作者：
Baxter, Rolf H.;Leach, Michael J. V.;Robertson, Neil M.
这封信提出了新的基于行为的跟踪人在低分辨率使用瞬时先验介导的头部姿势。我们扩展卡尔曼滤波器，自适应联合收割机运动信息与瞬时先验信念的人会去他们目前正在寻找的基础上。我们将这种新方法应用于行人监控，使用自动导出的头部姿势估计，虽然理论并不限于头部姿势先验。我们进行了行人凝视行为的统计分析，并展示了一组模拟和真实的行人观察跟踪性能。我们表明，通过使用瞬时的“故意”先验，我们的算法显着优于一个标准的卡尔曼滤波器的综合测试数据。
This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous 'intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.