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arXiv · 1407.0717

Deep Poselets for Human Detection

Lubomir Bourdev·Fei Yang·Rob Fergus
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Abstract

We address the problem of detecting people in natural scenes using a part approach based on poselets. We propose a bootstrapping method that allows us to collect millions of weakly labeled examples for each poselet type. We use these examples to train a Convolutional Neural Net to discriminate different poselet types and separate them from the background class. We then use the trained CNN as a way to represent poselet patches with a Pose Discriminative Feature (PDF) vector -- a compact 256-dimensional feature vector that is effective at discriminating pose from appearance. We train the poselet model on top of PDF features and combine them with object-level CNNs for detection and bounding box prediction. The resulting model leads to state-of-the-art performance for human detection on the PASCAL datasets.

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Lubomir Bourdev, Fei Yang, Rob Fergus. 2014-07-02. Deep Poselets for Human Detection. https://arxiv.org/abs/1407.0717

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