Robust pedestrian detection via a recursive convolution neural network

Pedestrian detection is fundamental challenge for computer vision which requires localizing objects within an image. Convolutional neural networks are widely used in object recognition. However, ordinal convolutional methods using sliding window as the input for networks require time to run an entire image and can only handle a fixed size window image. We propose to using a region proposal based in the V-disparity method to obtain prospect regions, instead of the original scanning methods to obtain the object regions. The region proposals from the V-disparity will be fed as the input for a convolutional neural network(CNN). We also extend the CNN for more effective task object detection. In our model, CNN are combined with recursive neural networks to learn features and classify color images. The convolutional neural network layer learns low-level features of the input image. By using CNN, the learned features can represent highly variable objects in the input image. The learned features after the convolutional layer are then given as inputs to a recursive neural network (RNN) to compose higher order features. The RNN is a multiple, fixed-tree recursive which can combine convolution and pooling into one efficient hierarchical operation.