Multiple Granularity Group Interaction Prediction
Most human activity analysis works (i.e., recognition orãprediction) only focus on a single granularity, i.e., eitherãmodelling global motion based on the coarse level movement such as human trajectories orãforecasting future detailed action based on body partsâ movement such as skeleton motion. In contrast, in this work, we propose a multi-granularity interaction prediction network which integratesãboth global motion and detailed local action. Built on a bi- directional LSTM network, theãproposed method possessesãbetween granularities links which encourage feature sharing as well as cross-feature consistency between both globalãand local granularity (e.g., trajectory or local action), and in turn predict long-term global location and local dynamics of each individual. We validate our method on severalãpublic datasets with promising performance.
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