End-to-end people detection in crowded scenes
Abstract: Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes.
Paper Link: https://arxiv.org/pdf/1506.04878.pdf
Conference : CVPR 2016
1. Implement the architecture proposed in the paper.
2. Implement the proper dataloader with any 2 data augmentation techniques.
3. Compare results to the ones in the paper.
4. You can use any framework you want.
Dataset Use any publicly available crowd detection dataset. You may use https://www.crowdhuman.org/