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Seg2Pose: Pose Esti...
Seg2Pose: Pose Estimations from Instance Segmentation Masks in One or Multiple Views for Traffic Applications
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- Ahrnbom, Martin (author)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Persson, Ivar (author)
- Lund University,Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Nilsson, Mikael (author)
- Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- SCITEPRESS - Science and Technology Publications, 2022
- 2022
- Swedish 8 s.
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In: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISAPP. - : SCITEPRESS - Science and Technology Publications. - 9789897585555 ; 5, s. 777-784
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Abstract
Subject headings
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- A system we denote Seg2Pose is presented which converts pixel coordinate tracks, represented by instance segmentation masks across multiple video frames, into world coordinate pose tracks, for road users seen by static surveillance cameras. The road users are bound to a ground surface represented by a number of 3D points and does not necessarily have to be perfectly flat. The system works with one or more views, by using a late fusion scheme. An approximate position, denoted the normal position, is computed from the camera calibration, per-class default heights and the ground surface model. The position is then refined a novel Convolutional Neural Network we denote Seg2PoseNet, taking instance segmentations and cropping positioning as its input. We evaluate this system quantitatively both on synthetic data from CARLA Simulator and on a real recording from a trinocular camera. The system outperforms the baseline method of only using the normal positions, which is roughly equivalent of a typical 2D to 3D conversion system, in both datasets.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
Keyword
- Pose Estimation
- Instance Segmentation
- Convolutional Neural Network
- Traffic Safety
- Road Users
- Tracking
- Stereo Camera
- Trinocular Camera Array
- Traffic Surveillance
Publication and Content Type
- kon (subject category)
- ref (subject category)
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