Multi-part boosting LSTMS for skeleton based human activity analysis
This paper presents the methods we adopt for ICME2017 large scale 3D human activity analysis challenge in depth videos. There are two tasks including the segmented activity recognition and the untrimmed activity detection. We adopt a network structure comprised of two LSTM layers, a fully connected layer and a softmax layer. For the recognition task, we investigate several schemes to handle the inconsistency of subject number of different activities. For the detection task, we trim multi-scale segment parts from untrimmed videos and adopt a proposal network followed by a classification network.