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Object Detection Using Cascaded Classification Models

Building a robust object detector for robots is alway essential for many robot operations. The traditional problem of object detection and recognition has been tackled by computer vision researchers for years [4, 5, 6, 7, 8, 13, 15]. There are many different approaches ranging from location, geometric context, cultural context [6, 8] to functionalities and categories of the objects. Most of these methods were developed based on very sophisticated computer vision algorithm and machine learning techniques with a single source of visualization camera images. The other source of visualization such as 3D sensor has not been well taken into account. The recent technique of Cascaded Classification Models is applied for less sophisticated machine learning models and some of them gives good results [20]. This projects employs Cascaded Classification Models (CCM) to build a robust object detector given two main sources of visualization usually available on a robot: camera images and 3D point cloud data from a depth sensor.