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Current people detectors operate either by scanning an image in a slidingwindow fashion or by classifying a discrete set of proposals. We propose amodel that is based on decoding an image into a set of people detections. Oursystem takes an image as input and directly outputs a set of distinct detectionhypotheses. Because we generate predictions jointly, common post-processingsteps such as non-maximum suppression are unnecessary. We use a recurrent LSTMlayer for sequence generation and train our model end-to-end with a new lossfunction that operates on sets of detections. We demonstrate the effectivenessof our approach on the challenging task of detecting people in crowded scenes.

End-to-end people detection in crowded scenes