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将多目标跟踪解释为动态概率模型中的资源约束近似推理Explaining Human Multiple Object Tracking as Resource-Constrained Approximate Inference in a Dynamic Probabilistic Model |
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| 课程网址: | http://videolectures.net/nips09_vul_ehmo/ |
| 主讲教师: | Edward Vul |
| 开课单位: | 麻省理工学院 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multiple object tracking is a task commonly used to investigate the architecture of human visual attention. Human participants show a distinctive pattern of successes and failures in tracking experiments that is often attributed to limits on an object system, a tracking module, or other specialized cognitive structures. Here we use a computational analysis of the task of object tracking to ask which human failures arise from cognitive limitations and which are consequences of inevitable perceptual uncertainty in the tracking task. We find that many human performance phenomena, measured through novel behavioral experiments, are naturally produced by the operation of our ideal observer model (a Rao-Blackwelized particle filter). The tradeoff between the speed and number of objects being tracked, however, can only arise from the allocation of a flexible cognitive resource, which can be formalized as either memory or attention. |
| 关 键 词: | 多目标跟踪; 人类视觉; 对象系统 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 20 |