CAJ | 학술논문

文中提出一种基于时空单词的两人交互行为识别方法,该方法从行为视频中提取丰富的时空兴趣点,基于人体剪影的连通性分析和时空兴趣点的历史信息,把时空兴趣点划分给不同的人体,并在兴趣点样本空间聚类生成时空码本(spatial-temporal codebook).对于给定的时空兴趣点集,通过投票得到表示单人原子行为的时空单词(spatial-temporal words).采用条件随机场模型建模单人原子行为,在两人交互行为的语义建模过程中,人工建立表示领域知识(domain knowledge)的一阶逻辑知识库,并训练马尔可夫逻辑网用以两人交互行为的推理.两人交互行为库上的实验结果证明了该方法的有效性.
문중제출일충기우시공단사적량인교호행위식별방법,해방법종행위시빈중제취봉부적시공흥취점,기우인체전영적련통성분석화시공흥취점적역사신식,파시공흥취점화분급불동적인체,병재흥취점양본공간취류생성시공마본(spatial-temporal codebook).대우급정적시공흥취점집,통과투표득도표시단인원자행위적시공단사(spatial-temporal words).채용조건수궤장모형건모단인원자행위,재량인교호행위적어의건모과정중,인공건립표시영역지식(domain knowledge)적일계라집지식고,병훈련마이가부라집망용이량인교호행위적추리.량인교호행위고상적실험결과증명료해방법적유효성.
This paper proposes a hierarchical approach for recognizing person-to-person interaction in indoor scenario from a single view, which is based on spatial-temporal feature extraction and representation. The dense space-time interest points detected from videos are divided into two sets exclusively according to the history information along the evolvement and the connectivity of the two human silhouettes. Then K-means clustering performs on points in the training set and learns the spatial-temporal codebook. For a given set of interest points, a spatial-temporal word is built by allowing each point to vote softly into the few centers nearest to it and accumulating the scores of all the points. The Conditional Random Field(CRF) whose inputs are the spatial-temporal words is used to modeling the primitive actions for each person, and common sense domain knowledge and first order logic production rules with weights are employed to learn the structure and the parameters of Markov Logic Network(MLN). The MLN can naturally integrate common sense reasoning with uncertain analysis, which is capable to deal with the uncertainty produced by CRF. Experiment results on the interaction dataset are provided to demonstrate the effectiveness and the robustness.