Object detection and feature base learning with sparse convolutional neural networks

A new convolutional neural network model termed sparse convolutional neural network (SCNN) is presented and its usefulness for real-time object detection in gray-valued, monocular video sequences is demonstrated. SCNNs are trained on "raw" gray values and are intended to perform feature selection as a part of regular neural network training. For this purpose, the learning rule is extended by an unsupervised component which performs a local nonlinear principal components analysis: in this way, meaningful and diverse properties can be computed from local image patches. The SCNN model can be used to train classifiers for different object classes which share a common first layer, i.e., a common preprocessing. This is of advantage since the information needs only to be calculated once for all classifiers. It is further demonstrated how SCNNs can be implemented by successive convolutions of the input image: scanning an image for objects at all possible locations is shown to be possible in real-time using this technique.