RGB-D image-based Object Detection: from Traditional Methods to Deep Learning Techniques
It provides a comprehensive overview for researchers and practitioners in computer vision, but is incremental as it reviews existing work rather than presenting new findings.
This chapter surveys RGB-D object detection methods, covering traditional hand-crafted features with machine learning and recent deep learning techniques that have revolutionized the field by achieving unprecedented performance levels.
Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of low cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations, and highlight some important directions for future research.