科研之友
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摘要
The recognition of activities of daily living (ADLs) refers to the classification of activities commonly carried out in daily life, which are of particular interest in numerous applications, such as health monitoring, smart home environments and surveillance systems. We introduce a novel method for activity recognition, which achieves high recognition rates in ADL scenarios, comparable to, or better than, the State-of-the-Art (SoA). Meaningful areas of interest, the motion boundary activity areas, are introduced for dense sampling of interest points, significantly reducing the computational cost of action representation. Interest points are tracked over time by an enhanced Kanade Lucas Tomasi (KLT) tracker and accumulated in a three-dimensional trajectory structure, where multi-scale descriptors are formed. The temporal length of trajectories is determined online in an adaptive, non ad-hoc manner, by applying sequential statistical change detection on motion features via the Cumulative Sum (CUSUM) method. We thus build a multi-scale hybrid local-global appearance and motion descriptor, invariant to temporal changes, and supplemented with global location information. Encoding is performed using both standard Bag-of-Visual-Words (BoVW) and Fisher scheme, while a multiclass Support Vector Machine (SVM) is trained for recognition purposes. Extensive experimentation took place on both benchmark and our own data-sets, where it was demonstrated that our algorithm is robust to a wide range of viewing/recording conditions and human activities, achieving human activity recognition that is more accurate or comparable with the SoA.
- 出版日期2015
