Prior knowledge-based deep learning method for indoor object recognition and application. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Ding, Xintao; Luo, Yonglong; Li, Qingde; Cheng, Yongqiang; Cai, Guorong; Munnoch, Robert; Xue, Dongfei; Yu, Qingying; Zheng, Xiaoyao; Wang, Bing
• Is Part Of: Systems science & control engineering. Volume 6:Issue 1(2018)
• Page Start: 249
• Page End: 257
• Publication Date: 2018-01-01
• Subjects: Indoor object recognition -- deep learning -- prior knowledge; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2018.1482477
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision.