Object recognition from 3D depth data with Extreme Learning Machine and Local Receptive Field

Object recognition from depth sensors has recently emerged as a renowned and challenging research topic. The current systems often require large amounts of time to train the models and to classify new data. In this work, we present a novel fast approach for object recognition from 3D data acquired from depth sensors such as Structure or Kinect sensors. We first extract simple but effective frame-level features from the raw depth data and build a recognition system based on extreme learning machine with a local receptive field. We test the presented method on two datasets: A self-collected dataset and the benchmark RGB-D object dataset. Experiments on both datasets show the effectiveness of our presented approach compared with the state-of-the-art methods. Fast computational time and high recognition accuracy make the presented method readily applicable for online recognition applications.