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Description: 优于Kalman滤波的粒子滤波在目标跟踪中的应用，幻灯片-Kalman filtering is superior to the particle filter in the target tracking application, slide
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Size: 431104 |
Author: zhao xiaowei |
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Description: 一篇介绍粒子滤波的资料，介绍的很详细，适合研究粒子滤波的初学者，希望对大家有帮助-Introduce a particle filter information on the very detailed study of particle filter is suitable for beginners
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Size: 431104 |
Author: xiankong |
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Description: 该代码用于实现粒子滤波视觉目标跟踪(PF)、卡尔曼粒子滤波视觉目标跟踪(KPF)、无迹粒子滤波视觉目标跟踪(UPF)。它们是本人这两年来编写的核心代码，用于实现鲁棒的视觉目标跟踪，其鲁棒性远远超越MeanShift(均值转移)和Camshift之类。用于实现视觉目标跟踪的KPF和UPF都是本人花费精力完成，大家在网上是找不到相关代码的。这些代码虽然只做了部分代码优化，但其优化版本已经成功应用于我们研究组研发的主动视觉目标跟踪打击平台中。现在把它们奉献给大家！-These codes are used to realize particle filter based visual object tracking (PF), kalman particle filter based visual object tracking, unscented particle filter based visual object tracking. Their robustness is far beyond the classical visual object tracking algorithms such as Mean-Shift (MeanShift) and CamShift。The codes of KPF and UPF for visual object tracking cost a great of my energy, and you can not find any relating algorithm codes on internet! Our research group have optimized these codes and applied them to develop a platform for active visual object tracking. Now, I dedicate them to you and wish you love them!
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Size: 396288 |
Author: 朱亮亮 |
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Description: 优于Kalman滤波的粒子滤波在目标跟踪中的应用-Beyond the Kalman Filter Particle filter for tracking applications
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Size: 431104 |
Author: 刘健 |
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Description: 消息传递算法用于信号处理的绝对经典之作，好好学习，天天向上-The message-passing approach to model-based
signal processing is developed with a focus on Gaussian
message passing in linear state-space models, which includes
recursive least squares, linear minimum-mean-squared-error
estimation, and Kalman filtering algorithms. Tabulated message
computation rules for the building blocks of linear models
allow us to compose a variety of such algorithms without
additional derivations or computations. Beyond the Gaussian
case, it is emphasized that the message-passing approach
encourages us to mix and match different algorithmic techniques,
which is exemplified by two different approachesV
steepest descent and expectation maximizationVto message
passing through a multiplier node.
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Size: 955392 |
Author: wfs |
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