Affiliation:
1. Zhengzhou University
2. Nankai University
3. Angle AI (Tianjin) Technology co. LTD
4. University of Louisiana at Lafayette
Abstract
In this work, we demonstrate an innovative object detection framework based on depth and active infrared intensity images fusion with a time-of-flight (ToF) camera. A slide window weight fusion (SWWF) method provides fuse image with two modalities to localize targets. Then, the depth and intensity information is extracted to construct a joint feature space. Next, we utilize four machine learning methods to achieve object recognition. To verify this method, experiments are performed on an in-house dataset containing 1066 images, which are categorized into six different surface materials. Consequently, the approach performs well on localization with a 0.778 intersection over union (IoU). The best classification results are obtained with K-Nearest Neighbor (KNN) with a 98.01% total accuracy. Furthermore, our demonstrated method is less affected by various illumination conditions.
Funder
Shaanxi Province Innovation Talent Promotion Program-Science and Technology Innovation Team
Subject
Atomic and Molecular Physics, and Optics