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Comparison of keypoint detection methods for indoor and outdoor scene recognition

Libal U., Łoziuk Ł.

Journal of Automation Mobile Robotics and Intelligent Systems

2017

Vol. 11, No. 4

7--14

We describe an experimental study, based on several million video scenes, of seven keypoint detection algorithms: BRISK, FAST, GFTT, HARRIS, MSER, ORB and STAR. It was observed that the probability distributions of selected keypoints are drastically different between indoor and outdoor environments for all algorithms analyzed. This paper presents a simple method for distinguishing between indoor and outdoor environments in a video sequence. The proposed method is based on the central location of keypoints in video frames. This has lead to a universally effective indoor/outdoor environment recognition method, and may prove to be a crucial step in the design of robotic control algorithms based on computer vision, especially for autonomous mobile robots.

Fusion of door and corner features for scene recognition

Chacon-Murguia M. I., Sandoval-Rodriguez R., Guerrero-Saucedo C. P.

Journal of Automation Mobile Robotics and Intelligent Systems

2011

Vol. 5, No. 1

68-76

Scene recognition is a paramount task for autonomous systems that navigate in open scenarios. In order to achie ve high scene recognition performance it is necesary to use correct information. Therefore, data fusion is beco ming a paramount point in the design of scene recognition systems. This paper presents a scenery recognition system using a neural network hierarchical approach. The system is based on information fusion in indoor scenarios. The system extracts relevant information with respect to color and landmarks. Color information is related mainly to localization of doors. Landmarks are related to corner de tection. The corner detection method proposed in the pa per based on corner detection windows has 99% detection of real corners and 13.43% of false positives. The hierar chical neural systems consist on two levels. The first level is built with one neural network and the second level with two. The hierarchical neural system, based on feed for ward architectures, presents 90% of correct recognition in the first level in training, and 95% in validation. The first ANN in the second level shows 90.90% of correct recogni tion during training, and 87.5% in validation. The second ANN has a performance of 93.75% and 91.66% during training and validation, respectively. The total perfor mance of the systems was 86.6% during training, and 90% in validation.