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Classifying Pedestrian Actions In Advance Using Predicted Video of Urban Driving Scenes

We explore prediction of urban pedestrian actions by generating a video future of the traffic scene, and show promising results in classifying pedestrian behaviour before it is observed. We compare several encoder-decoder network models that predict 16 frames (400-600 milliseconds of video) from the preceding 16 frames.

Our main contribution is a method for learning a sequence of representations to iteratively transform features learnt from the input to the future. Then we use a binary action classifier network for determining a pedestrian’s crossing intent from predicted video. Our results show an average precision of 81%, significantly higher than previous methods. The model with the best classification performance runs for 117 ms on commodity GPU, giving an effective look- ahead of 416 ms.

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