图 书 馆
多尺度活动识别的成本敏感自顶向下/自下而上推理Cost-Sensitive Top-Down/Bottom-Up Inference for Multiscale Activity Recognition |
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| 课程网址: | http://videolectures.net/eccv2012_amer_recognition/ |
| 主讲教师: | Mohamed Amer |
| 开课单位: | 俄勒冈州立大学 |
| 开课时间: | 2012-11-12 |
| 课程语种: | 英语 |
| 中文简介: |  本文解决了一个新问题，即多尺度活动识别。我们的目标是检测和定位范围广泛的活动，包括个人活动和小组活动，这些活动可能同时在高分辨率视频中同时发生。视频分辨率允许数字放大（或缩小），以检查识别所需的精细细节（或更粗的比例）。关键的挑战是如何避免在所有时空尺度上运行大量的探测器，而如何实现整体上一致的视频解释。为此，我们使用三层“或”图共同建模小组活动，单个动作和参与对象。 AND OR图允许通过探索利用策略对有效，成本敏感的推理进行原则性表述。我们的推论最佳地调度了以下计算过程：1）直接应用称为α过程的活动检测器； 2）基于检测活动部分的自下而上的推理，称为β过程； 3）基于检测活动上下文的自上而下的推理，称为γ过程。调度迭代地最大化所生成的解析图的对数后验。为了进行评估，我们编译了一个新的数据集并对其进行了基准测试，该数据集是在UCLA校园的一个院落中发生的集体和个人活动的高分辨率视频的。 |
| 课程简介: | This paper addresses a new problem, that of multiscale activity recognition. Our goal is to detect and localize a wide range of activities, including individual actions and group activities, which may simultaneously co-occur in high-resolution video. The video resolution allows for digital zoom-in (or zoom-out) for examining fine details (or coarser scales), as needed for recognition. The key challenge is how to avoid running a multitude of detectors at all spatiotemporal scales, and yet arrive at a holistically consistent video interpretation. To this end, we use a three-layered AND-OR graph to jointly model group activities, individual actions, and participating objects. The AND-OR graph allows a principled formulation of efficient, cost-sensitive inference via an explore-exploit strategy. Our inference optimally schedules the following computational processes: 1) direct application of activity detectors-called α process; 2) bottom-up inference based on detecting activity parts-called β process; and 3) top-down inference based on detecting activity context-called γ process. The scheduling iteratively maximizes the log-posteriors of the resulting parse graphs. For evaluation, we have compiled and benchmarked a new dataset of high-resolution videos of group and individual activities co-occurring in a courtyard of the UCLA campus. |
| 关 键 词: | 视频分辨; 解析图 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-04-07：zyk |
| 最后编审: | 2021-04-07：zyk |
| 阅读次数: | 66 |