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In this study we investigate with ECs the problem of face pose alignment, which is an essential pre-processing stage for facial processing pipelines. EC-based alignment can unlock all these benefits in facial applications, especially where motion and dynamics carry the most relevant information due to the temporal change event sensing. We specifically aim at efficient processing by developing a coarse alignment method to handle large pose variations in facial applications. For this purpose, we have prepared by multiple human annotations a dataset of extreme head rotations with varying motion intensity. We propose a motion detection based alignment approach in order to generate activity dependent pose-events that prevents unnecessary computations in the absence of pose change. The alignment is realized by cascaded regression of extremely randomized trees. Since EC sensors perform temporal differentiation, we characterize the performance of the alignment in terms of different levels of head movement speeds and face localization uncertainty ranges as well as face resolution and predictor complexity. Our method obtained 2.7% alignment failure on average, whereas annotator disagreement was 1%. The promising coarse alignment performance on EC sensor data together with a comprehensive analysis demonstrate the potential of ECs in facial applications.<\/jats:p>","DOI":"10.3390\/s20247079","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T08:59:34Z","timestamp":1607590774000},"page":"7079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Face Pose Alignment with Event Cameras"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5142-6384","authenticated-orcid":false,"given":"Arman","family":"Savran","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Yasar University, 35100 Izmir, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3465-6449","authenticated-orcid":false,"given":"Chiara","family":"Bartolozzi","sequence":"additional","affiliation":[{"name":"Event-Driven Perception for Robotics, Istituto Italiano di Tecnologia, 16163 Genova, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/JSSC.2014.2342715","article-title":"A 240 \u00d7 180 130 dB 3 \u03bcs Latency Global Shutter Spatiotemporal Vision Sensor","volume":"49","author":"Brandli","year":"2014","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1109\/JSSC.2010.2085952","article-title":"A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS","volume":"46","author":"Posch","year":"2010","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11263-018-1097-z","article-title":"Facial Landmark Detection: A Literature Survey","volume":"127","author":"Wu","year":"2018","journal-title":"IJCV"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.08.008","article-title":"Face Alignment In-the-wild","volume":"162","author":"Jin","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017, January 22\u201329). How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks). Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.116"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Savran, A., Tavarone, R., Higy, B., Badino, L., and Bartolozzi, C. (2018, January 15\u201319). Energy and Computation Efficient Audio-Visual Voice Activity Detection Driven by Event-Cameras. Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00055"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, X., Neil, D., Delbruck, T., and Liu, S. (2019, January 26\u201329). Lip Reading Deep Network Exploiting Multi-Modal Spiking Visual and Auditory Sensors. Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan.","DOI":"10.1109\/ISCAS.2019.8702565"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1109\/TPAMI.2016.2574707","article-title":"HOTS: A Hierarchy Of event-based Time-Surfaces for pattern recognition","volume":"39","author":"Lagorce","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kazemi, V., and Sullivan, J. (2014, January 23\u201328). One Millisecond Face Alignment with an Ensemble of Regression Trees. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ren, S., Cao, X., Wei, Y., and Sun, J. (2014, January 23\u201328). Face Alignment at 3000 FPS via Regressing Local Binary Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.218"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/TPAMI.2017.2778152","article-title":"Face Alignment in Full Pose Range: A 3D Total Solution","volume":"41","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"Sun, K., Wu, W., Liu, T., Yang, S., Wang, Q., Zhou, Q., Ye, Z., and Qian, C. (November, January 27). FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Miao, X., Zhen, X., Liu, X., Deng, C., Athitsos, V., and Huang, H. (2018, January 18\u201322). Direct Shape Regression Networks for End-to-End Face Alignment. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00529"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1109\/TPAMI.2017.2787130","article-title":"Facial Landmark Detection with Tweaked Convolutional Neural Networks","volume":"40","author":"Wu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2018, January 18\u201322). Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00019"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dong, X., Yu, S.I., Weng, X., Wei, S.E., Yang, Y., and Sheikh, Y. (2018, January 18\u201322). Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00045"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Merget, D., Rock, M., and Rigoll, G. (2018, January 18\u201322). Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00088"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., and Kautz, J. (2018, January 18\u201322). Improving Landmark Localization With Semi-Supervised Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00167"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Feng, Z.H., Kittler, J., Awais, M., Huber, P., and Wu, X.J. (2018, January 18\u201322). Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00238"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/s11263-018-1095-1","article-title":"RED-Net: A Recurrent Encoder-Decoder Network for Video-Based Face Alignment","volume":"126","author":"Peng","year":"2018","journal-title":"IJCV"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xiao, S., Feng, J., Liu, L., Nie, X., Wang, W., Yan, S., and Kassim, A.A. (2017, January 21\u201326). Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.181"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jourabloo, A., Liu, X., Ye, M., and Ren, L. (2017, January 21\u201326). Pose-Invariant Face Alignment with a Single CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.347"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bhagavatula, C., Zhu, C., Luu, K., and Savvides, M. (2017, January 21\u201326). Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.429"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1109\/TPAMI.2017.2734779","article-title":"Two-Stream Transformer Networks for Video-Based Face Alignment","volume":"40","author":"Liu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lv, J., Shao, X., Xing, J., Cheng, C., and Zhou, X. (2017, January 21\u201326). A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.393"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017, January 21\u201326). Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.400"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gu, J., Yang, X., Mello, S.D., and Kautz, J. (2017, January 21\u201326). Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.167"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Trigeorgis, G., Snape, P., Nicolaou, M.A., Antonakos, E., and Zafeiriou, S. (2016, January 27\u201330). Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.453"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shan, S., Kan, M., and Chen, X. (2014, January 6\u201312). Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kan, M., Shan, S., and Chen, X. (2016, January 27\u201330). Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.373"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Ji, Q. (2015, January 7\u201313). Robust Facial Landmark Detection Under Significant Head Poses and Occlusion. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.417"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Manderscheid, J., Sironi, A., Bourdis, N., Migliore, D., and Lepetit, V. (2019, January 16\u201320). Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01049"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"148075","DOI":"10.1109\/ACCESS.2020.3015759","article-title":"Hybrid Deblur Net: Deep Non-Uniform Deblurring with Event Camera","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., and Scaramuzza, D. (2019, January 16\u201320). Events-To-Video: Bringing Modern Computer Vision to Event Cameras. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00398"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Maqueda, A.I., Loquercio, A., Gallego, G., Garc\u00eda, N., and Scaramuzza, D. (2018, January 18\u201323). Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars. Proceedings of the 2018 Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00568"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., and Welinder, P. (2010, January 13\u201318). Cascaded pose regression. Proceedings of the 2010 Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540094"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Burgos-Artizzu, X., Perona, P., and Doll\u00e1r, P. (2013, January 1\u20138). Robust Face Landmark Estimation Under Occlusion. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.191"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xiong, X., and la Torre, F.D. (2013, January 23\u201328). Supervised Descent Method and Its Applications to Face Alignment. Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.75"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s11263-013-0667-3","article-title":"Face Alignment by Explicit Shape Regression","volume":"107","author":"Cao","year":"2014","journal-title":"IJCV"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, D., Ren, S., Wei, Y., Cao, X., and Sun, J. (2014, January 6\u201312). Joint Cascade Face Detection and Alignment. Proceedings of the 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_8"},{"key":"ref_42","unstructured":"Zhu, S., Li, C., Loy, C.C., and Tang, X. (2015, January 7\u201312). Face alignment by coarse-to-fine shape searching. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xiao, S., Yan, S., and Kassim, A.A. (2015, January 7\u201313). Facial Landmark Detection via Progressive Initialization. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, ICCV Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.130"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lee, D., Park, H., and Yoo, C.D. (2015, January 7\u201312). Face alignment using cascade Gaussian process regression trees. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299048"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11263-017-0999-5","article-title":"A Comprehensive Performance Evaluation of Deformable Face Tracking \u201cIn-the-Wild\u201d","volume":"126","author":"Chrysos","year":"2018","journal-title":"IJCV"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, J., Deng, J., Zhang, K., and Liu, Q. (2015, January 7\u201313). Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, ICCV Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.131"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Lozano, E., Mart\u00ednez, B., Tzimiropoulos, G., and Valstar, M.F. (2016, January 11\u201314). Cascaded Continuous Regression for Real-Time Incremental Face Tracking. Proceedings of the 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_39"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Khan, M.H., McDonagh, J., and Tzimiropoulos, G. (2017, January 22\u201329). Synergy between Face Alignment and Tracking via Discriminative Global Consensus Optimization. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.409"},{"key":"ref_49","unstructured":"Gallego, G., Delbr\u00fcck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A.J., Conradt, J., and Daniilidis, K. (2019). Event-based Vision: A Survey. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gehrig, D., Loquercio, A., Derpanis, K.G., and Scaramuzza, D. (November, January 27). End-to-End Learning of Representations for Asynchronous Event-Based Data. Proceedings of the 2019 International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00573"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Barua, S., Miyatani, Y., and Veeraraghavan, A. (2016, January 7\u20139). Direct face detection and video reconstruction from event cameras. Proceedings of the EEE Winter Conference on Application of Computer Vision, Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477561"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1109\/TNNLS.2015.2401834","article-title":"An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking","volume":"26","author":"Valeiras","year":"2015","journal-title":"IEEE Trans. Neu. Netw. Learn. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3389\/fnins.2020.00587","article-title":"Event-Based Face Detection and Tracking Using the Dynamics of Eye Blinks","volume":"14","author":"Lenz","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bardow, P., Davison, A.J., and Leutenegger, S. (2016, January 27\u201330). Simultaneous Optical Flow and Intensity Estimation from an Event Camera. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.102"},{"key":"ref_55","unstructured":"Delbruck, T., Hu, Y., and He, Z. (2020). V2E: From video frames to realistic DVS event camera streams. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1177\/0278364917691115","article-title":"The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM","volume":"36","author":"Mueggler","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/BF02291478","article-title":"Generalized procrustes analysis","volume":"40","author":"Gower","year":"1975","journal-title":"Psychometrika"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.imavis.2016.01.002","article-title":"300 Faces In-The-Wild Challenge","volume":"47","author":"Sagonas","year":"2016","journal-title":"Image Vis. Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1162\/NECO_a_00703","article-title":"What can neuromorphic event-driven precise timing add to spike-based pattern recognition?","volume":"27","author":"Akolkar","year":"2015","journal-title":"Neural Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R., and Scaramuzza, D. (2020, January 1\u20135). Fast image reconstruction with an event camera. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093366"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Son, B., Suh, Y., Kim, S., Jung, H., Kim, J.S., Shin, C., Park, K., Lee, K., Park, J., and Woo, J. (2017, January 5\u20139). 4.1 A 640 \u00d7 480 dynamic vision sensor with a 9 \u03bcm pixel and 300Meps address-event representation. Proceedings of the 2017 IEEE International Solid-State Circuits Conference (ISSCC), Francisco, CA, USA.","DOI":"10.1109\/ISSCC.2017.7870263"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","article-title":"A 128 \u00d7 128 120 dB 15 \u03bcs Latency Asynchronous Temporal Contrast Vision Sensor","volume":"43","author":"Lichtsteiner","year":"2008","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tarkoma, S., Siekkinen, M., Lagerspetz, E., and Xiao, Y. (2014). Smartphone Energy Consumption: Modeling and Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9781107326279"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/LRA.2018.2793357","article-title":"Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios","volume":"3","author":"Vidal","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Guo, M., Huang, J., and Chen, S. (2017, January 28\u201331). Live demonstration: A 768 \u00d7 640 pixels 200Meps dynamic vision sensor. Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MA, USA.","DOI":"10.1109\/ISCAS.2017.8050397"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:43:18Z","timestamp":1760179398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":66,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247079"],"URL":"https:\/\/doi.org\/10.3390\/s20247079","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,10]]}}}