| Summary: | Currently,the recognition technology targeted at human action in an interior scene is widely used in video content understanding,home-based care,medical care and other fields,and existing researches pay more heed to the modelling of human action,while ignoring the connection between interior scene and human action in videos.With a view to making full use of the relevance between the scene information and the human motion,this paper studies the recognition approaches for human action in an interior scene based on scene-prior knowledge.Yet,the paper proposes scene-prior knowledge inflated 3D ConvNet(SPI3D).Firstly,the ResNet152 network is adopted to extract scene features for scene classification.Then,based on the results,combined with scene-prior knowledge,this paper introduces quantified scene prior knowledge,optimizes the overall objective function by constraining the weights.Additionally,aiming at the problem that most of the existing data sets focus on the characteristics of human action,whereas the scene information remains complex and plain,an interior scene-action database(SADB) is established.It is shown in experimental results,on the SADB,the recognition accuracy rate of SPI3D reaches 87.9%,6% higher than the recognition accuracy of I3D directly.It can be seen that the modelling for the recognition on human action in interior scene is featured by better performance after introducing the prior knowledge of the scene.
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