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Smart city article details
| Title | Stereo Imagery Based Depth Sensing In Diverse Outdoor Environments: Practical Considerations |
|---|---|
| ID_Doc | 53032 |
| Authors | Zhang Y.; Caspi A. |
| Year | 2019 |
| Published | Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, SCC 2019 |
| DOI | http://dx.doi.org/10.1145/3357492.3358627 |
| Abstract | Urban mapping for street-side environments (i.e., sidewalk environments rather than auto-centric ones) is just one of many new applications requiring simultaneous object detection and localization in outdoor scenes. Automated mapping has focused on car environments in support of vehicle travel, though pedestrian infrastructure maps, historical structure maps and recreational trail maps are as important in supporting urban life. Unfortunately, many of the auto-centric automated approaches cannot be leveraged in other outdoor use cases due to challenging and cost-prohibitive depth sensing hardware involving LiDar sensors and large computing resources. Using depth-sensing stereo cameras is cost-efficient, but little information is available detailing the best uses and efficacy of different systems in diverse outdoor environments. Our review highlights the complexity of localizing objects in real outdoor environments using stereo cameras and our comparative analysis approach to two common camera setups demonstrates the variation among depth-sensing cameras. We collect a modest dataset with side-by-side images for comparing two commonly used depth cameras, the ZED Stereo Camera (a passive stereo camera) and Intel® RealSense Depth Camera D435 (an active IR stereo camera). We contrast system performance in diverse urban environments, and provide accuracy of depth measurement results based on data collected from each stereo sensor. We focus our discussion on practical considerations and objects that are commonly presented in the street environment but usually hard to map by hand. © 2019 Copyright held by the owner/author(s). |
| Author Keywords | Depth sensing; First-last-mile; Image processing; Nonmotorized transportation; Object detection; Pedestrian mapping |
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