Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

Georgios Triantafyllidis, Michalis Dimitriou, Tsampikos Kounalakis, Nikolaos Vidakis

Abstract

This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method proposed by Jianchao Yang et al for the efficient classification of the detected objects. Experimental results are presented for both detection and classification, showing the efficiency of the proposed design.

 

Keywords

computer vision, Object Detection, Microsoft Kinect, Feature Extraction, Classification, Linear Spatial Pyramid Matching

Full Text:

PDF (3074Kb)
Copyright (c)