End-to-End People Detection in Crowded Scenes
| Autor: | Andrew Y. Ng, Mykhaylo Andriluka, Russell Stewart |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
Sequence Computer science business.industry 05 social sciences 02 engineering and technology Function (mathematics) Image (mathematics) Task (project management) Recurrent neural network Sliding window protocol 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Set (psychology) business |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2016.255 |
| Popis: | 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 nonmaximum 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 scenes1. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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