{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:21:00Z","timestamp":1742995260592,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442667"},{"type":"electronic","value":"9783031442674"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44267-4_22","type":"book-chapter","created":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:03:27Z","timestamp":1696118607000},"page":"389-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Safety Monitoring for\u00a0Pedestrian Detection in\u00a0Adverse Conditions"],"prefix":"10.1007","author":[{"given":"Swapnil","family":"Mallick","sequence":"first","affiliation":[]},{"given":"Shuvam","family":"Ghosal","sequence":"additional","affiliation":[]},{"given":"Anand","family":"Balakrishnan","sequence":"additional","affiliation":[]},{"given":"Jyotirmoy","family":"Deshmukh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"22_CR1","unstructured":"Autonomous Vehicle Collision Reports. Technical report, California Department of Motor Vehicles (2023). www.dmv.ca.gov\/portal\/vehicle-industry-services\/autonomous-vehicles\/autonomous-vehicle-collision-reports\/"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Antonante, P., Spivak, D.I., Carlone, L.: Monitoring and Diagnosability of Perception Systems. arXiv:2005.11816 [cs] (2020)","DOI":"10.1109\/IROS51168.2021.9636497"},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-88494-9_18","volume-title":"Runtime Verification","author":"A Balakrishnan","year":"2021","unstructured":"Balakrishnan, A., Deshmukh, J., Hoxha, B., Yamaguchi, T., Fainekos, G.: PerceMon: online monitoring for\u00a0perception systems. In: Feng, L., Fisman, D. (eds.) RV 2021. LNCS, vol. 12974, pp. 297\u2013308. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88494-9_18"},{"key":"22_CR4","doi-asserted-by":"publisher","unstructured":"Balakrishnan, A., et al.: Specifying and evaluating quality metrics for vision-based perception systems. In: 2019 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1433\u20131438 (2019). https:\/\/doi.org\/10.23919\/DATE.2019.8715114","DOI":"10.23919\/DATE.2019.8715114"},{"key":"22_CR5","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"22_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/978-3-030-03769-7_23","volume-title":"Runtime Verification","author":"A Dokhanchi","year":"2018","unstructured":"Dokhanchi, A., Amor, H.B., Deshmukh, J.V., Fainekos, G.: Evaluating perception systems for autonomous vehicles using quality temporal logic. In: Colombo, C., Leucker, M. (eds.) RV 2018. LNCS, vol. 11237, pp. 409\u2013416. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03769-7_23"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Dokhanchi, A., Hoxha, B., Tuncali, C.E., Fainekos, G.: An efficient algorithm for monitoring practical TPTL specifications. In: 2016 ACM\/IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE), pp. 184\u2013193. IEEE (2016)","DOI":"10.1109\/MEMCOD.2016.7797763"},{"issue":"1","key":"22_CR8","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015). https:\/\/doi.org\/10.1007\/s11263-014-0733-5","journal-title":"Int. J. Comput. Vision"},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"issue":"12","key":"22_CR11","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341\u20132353 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR12","unstructured":"Hekmatnejad, M.: Formalizing Safety, Perception, and Mission Requirements for Testing and Planning in Autonomous Vehicles. Ph.D. thesis, Arizona State University (2021)"},{"issue":"2","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3181974","volume":"37","author":"Y Hu","year":"2018","unstructured":"Hu, Y., He, H., Xu, C., Wang, B., Lin, S.: Exposure: a white-box photo post-processing framework. ACM Trans. Graph. (TOG) 37(2), 1\u201317 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"22_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., Ren, G., Yu, R., Guo, S., Zhu, J., Zhang, L.: Image-adaptive YOLO for object detection in adverse weather conditions. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"22_CR18","unstructured":"Michaelis, C., et al.: Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (2020). arXiv:1907.07484 [cs, stat]"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), vol. 1, pp. 598\u2013605. IEEE (2000)","DOI":"10.1109\/CVPR.2000.855874"},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Padilla, R., Netto, S.L., da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 237\u2013242 (2020). https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130, iSSN: 2157-8702","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"issue":"3","key":"22_CR21","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/electronics10030279","volume":"10","author":"R Padilla","year":"2021","unstructured":"Padilla, R., Passos, W.L., Dias, T.L.B., Netto, S.L., da Silva, E.A.B.: A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics 10(3), 279 (2021). https:\/\/doi.org\/10.3390\/electronics10030279","journal-title":"Electronics"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Qin, Q., Chang, K., Huang, M., Li, G.: DENet: detection-driven enhancement network for object detection under adverse weather conditions. In: Proceedings of the Asian Conference on Computer Vision, pp. 2813\u20132829 (2022)","DOI":"10.1007\/978-3-031-26313-2_30"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"22_CR24","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"22_CR25","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I\u2013I. IEEE (2001)","DOI":"10.1109\/CVPR.2001.990493"},{"key":"22_CR27","unstructured":"Teeti, I., Musat, V., Khan, S., Rast, A., Cuzzolin, F., Bradley, A.: Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing (2023). arXiv:2201.03246 [cs]"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Wu, B., Iandola, F., Jin, P.H., Keutzer, K.: SqueezeDet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 129\u2013137 (2017)","DOI":"10.1109\/CVPRW.2017.60"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., Darrell, T.: End-to-end learning of driving models from large-scale video datasets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2174\u20132182 (2017)","DOI":"10.1109\/CVPR.2017.376"},{"issue":"6","key":"22_CR30","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1109\/83.336245","volume":"3","author":"Y Xu","year":"1994","unstructured":"Xu, Y., Weaver, J.B., Healy, D.M., Lu, J.: Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans. Image Process. 3(6), 747\u2013758 (1994)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Lecture Notes in Computer Science","Runtime Verification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44267-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:35:15Z","timestamp":1730230515000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44267-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442667","9783031442674"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44267-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Runtime Verification","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thessaloniki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/rv23.csd.auth.gr","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,15","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The proceedings includes also 4 Tutorial Papers and 2 Invited Papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}