Pedestrian detection system for smart communities using deep Convolutional Neural Networks
| Autor: | Paul Rad, Patrick Benavidez, John J. Prevost, Mo Jamshidi, Jonathan Lwowski, Prasanna Kolar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Contextual image classification
Artificial neural network Computer science business.industry Pedestrian detection Deep learning 020208 electrical & electronic engineering Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Frame rate Convolutional neural network Convolution 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | SoSE |
| DOI: | 10.1109/sysose.2017.7994968 |
| Popis: | Pedestrian recognition is a key problem for a number of application domains namely autonomous driving, search and rescue, surveillance and robotics. Real-time pedestrian recognition entails determining if a pedestrian is in an image frame. State-of-art pedestrian detection convolution neural networks(CNN) such as Fast R-CNN depend on computationally expensive region detection algorithms to hypothesize pedestrian locations. This paper presents a simple, fast and very accurate approach by cascading fast regional detection and deep convolution networks. Convolution networks have been shown to excel at image classification. However, convolution networks are notoriously slow at inference time. In this work, we introduce a fast regional detection cascaded with deep convolution networks that enables real-time pedestrian detection that could be used to alert a driver if a pedestrian is on the roadway. The classification CNN has given an accuracy of 95.7%, with a processing rate of about 15 frames per second on a low performance system without a graphical processing unit (GPU). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|