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Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

2019-11-27 | |  52 |   41 |   0
Abstract Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful application of deep learning methods to vision, we report state-of-theart and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the ?lters at each stage.

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