Pose Invariant Object Recognition Using a Bag of Words Approach

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Costa, CM (Author) Other; Armando Jorge Sousa (Author) FEUP; Germano Veiga (Author) FEUP

Conference proceedings International

Title: Advances in Intelligent Systems and Computing

Pages: 153-164

3rd Iberian Robotics Conference, ROBOT 2017

22 November 2017 through 24 November 2017

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00N-B3W

DOI: 10.1007/978-3-319-70836-2_13

Abstract (EN): Pose invariant object detection and classification plays a critical role in robust image recognition systems and can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. This paper analyzes the usage of the Bag of Words model for recognizing objects in different scales, orientations and perspective views within cluttered environments. The recognition system relies on image analysis techniques, such as feature detection, description and clustering along with machine learning classifiers. For pinpointing the location of the target object, it is proposed a multiscale sliding window approach followed by a dynamic thresholding segmentation. The recognition system was tested with several configurations of feature detectors, descriptors and classifiers and achieved an accuracy of 87% when recognizing cars from an annotated dataset with 177 training images and 177 testing images. © Springer International Publishing AG 2018.

Language: English

Type (Professor's evaluation): Scientific