The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Event-Based Moving Object Detection and Tracking
Open accessAbstract
Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion efficiently and reliably in the most sophisticated scenarios, but these advantages come at a price - modern event-based vision sensors have extremely low resolution, produce a lot of noise and require the development of novel algorithms to handle the asynchronous event stream. This paper presents a new, efficient approach to object tracking with asynchronous cameras. We present a novel event stream representation which enables us to utilize information about the dynamic (temporal)component of the event stream. The 3D geometry of the event stream is approximated with a parametric model to motion-compensate for the camera (without feature tracking or explicit optical flow computation), and then moving objects that don't conform to the model are detected in an iterative process. We demonstrate our framework on the task of independent motion detection and tracking, where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion.
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Citations
Cites
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A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
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The perception of the visual world.
Boring, E. G. (1951) · Psychological Bulletin · doi:10.1037/h0051570 · Cited by 777
Vidal, A. R., Rebecq, H., Horstschaefer, T., & Scaramuzza, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2793357 · Cited by 541
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.062 · Cited by 470
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera
Kim, H., Leutenegger, S., & Davison, A. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_21 · Cited by 438
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00407 · Cited by 341
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-176051 · Cited by 271
Event-Based Visual Inertial Odometry
Zhu, A. Z., Atanasov, N., & Daniilidis, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.616 · Cited by 249
Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification
Ahmad, S., Scarpellini, G., Morerio, P., & Bue, A. D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00052 · Cited by 54
Person Re-Identification without Identification via Event Anonymization
Ahmad, S., Morerio, P., & Del Bue, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01022 · Cited by 39
Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing
Jawaid, M., Elms, E., Latif, Y., & Chin, T.-J. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160531 · Cited by 32
Ahmad, S., Morerio, P., & Del Bue, A. (2024) · IEEE Access · doi:10.1109/access.2024.3399539 · Cited by 16
“These include object recognition [1], human pose estimation [2], [3], detection and tracking [4], [5], [6], and person re-identification (ReId) [7].”
Background
Metadata
- Authors
- Mitrokhin, A., Fermuller, C., Parameshwara, C., & Aloimonos, Y.
- Venue
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1-9
- OpenAlex
- W2789435382
- Cited by
- 365
- References
- 26
- FWCI
- 9.23
- Type
- conference paper
- Language
- en
- Open access
- green
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- Mitrokhin, A., Fermuller, C., Parameshwara, C., & Aloimonos, Y. (2018). Event-Based Moving Object Detection and Tracking. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2018.8593805
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