MediaPipe pose landmarks fed to LSTM, GRU, and 1D CNN predict pedestrian crossing intent on a private 60-video dataset, with GRU reaching 89.24% test AUC and 1D CNN running at 1 ms inference.
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,
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Predicting Road Crossing Behaviour using Pose Detection and Sequence Modelling
MediaPipe pose landmarks fed to LSTM, GRU, and 1D CNN predict pedestrian crossing intent on a private 60-video dataset, with GRU reaching 89.24% test AUC and 1D CNN running at 1 ms inference.