Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

Understand

We propose a deep neural network fusion architecture for fast and robust pedestrian detection.

• The proposed network fusion architecture allows for parallel processing of multiple networks for speed.
• A single shot deep convolutional network is trained as a object detector to generate all possible pedestrian candidates of different sizes and occlusions.
• This network outputs a large variety of pedestrian candidates to cover the majority of ground-truth pedestrians while also introducing a large number of false positives.