Vehicle pose detection using region based convolutional neural network

In recent years, category-level object detection has gained a lot of attention. In addition to object localization, estimation of the object pose has practical applications in intelligent transportation, autonomous driving and robotics. Parts based models have been used for pose estimation in recent years, but these models depend on manual supervision or require a complex algorithm to locate the object parts. In this work, we have used Convolutional Neural Network for the pose estimation of vehicle in an image. The advantage of multiple classifications of objects at the same time motivates us to choose the convolutional neural network. We make use of state-of-the-art implementation of convolution neural network named the Region Based Convolutional Neural Network(FASTER-RCNN) for estimating the pose of vehicle. We annotate the comprehensive cars dataset of Stanford, required for training the model and upon testing we have achieved good results with good accuracy.