{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:05:35Z","timestamp":1768741535399,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Fundamental Research Funds for the Universities of Henan Province","award":["NSFRF220414"],"award-info":[{"award-number":["NSFRF220414"]}]},{"name":"Excellent Young Teachers Program of Henan Polytechnic University","award":["No. 2019XQG-02"],"award-info":[{"award-number":["No. 2019XQG-02"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00138-023-01439-6","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:02:38Z","timestamp":1692367358000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AFC-Net: adjacent feature complementary for crowded pedestrian detection"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-2111","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cailing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-5009","authenticated-orcid":false,"given":"Zhanqiang","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"1439_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107502","volume":"107","author":"L Bi","year":"2020","unstructured":"Bi, L., Feng, D.D., Fulham, M., Kim, J.: Multi-label classification of multi-modality skin lesion via hyper-connected convolutional neural network. Pattern Recogn. 107, 107502 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107502","journal-title":"Pattern Recogn."},{"key":"1439_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-021-01169-7","author":"X Ke","year":"2021","unstructured":"Ke, X., Lin, X., Qin, L.: Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios. Mach. Vis. Appl. (2021). https:\/\/doi.org\/10.1007\/s00138-021-01169-7","journal-title":"Mach. Vis. Appl."},{"key":"1439_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-022-01293-y","author":"CB Murthy","year":"2022","unstructured":"Murthy, C.B., Hashmi, M.F., Keskar, A.: Efficientlitedet: a real-time pedestrian and vehicle detection algorithm. Mach. Vis. Appl. (2022). https:\/\/doi.org\/10.1007\/s00138-022-01293-y","journal-title":"Mach. Vis. Appl."},{"key":"1439_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/app12147255","author":"H-K Jung","year":"2022","unstructured":"Jung, H.-K., Choi, G.-S.: Improved yolov5: efficient object detection using drone images under various conditions. Appl. Sci. (2022). https:\/\/doi.org\/10.3390\/app12147255","journal-title":"Appl. Sci."},{"key":"1439_CR5","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91\u2013110 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int. J. Comput. Vis."},{"key":"1439_CR6","doi-asserted-by":"publisher","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), volume 1. IEEE, vol. 2005, pp. 886\u2013893 (2005). https:\/\/doi.org\/10.1109\/CVPR.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"1439_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","volume":"356","author":"K Fu","year":"2019","unstructured":"Fu, K., Zhao, Q., Gu, I.Y.-H., Yang, J.: Deepside: a general deep framework for salient object detection. Neurocomputing 356, 69\u201382 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.04.062","journal-title":"Neurocomputing"},{"key":"1439_CR8","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/978-3-030-01240-3_45","volume-title":"Computer Vision\u2014ECCV 2018","author":"C Lin","year":"2018","unstructured":"Lin, C., Lu, J., Wang, G., Zhou, J.: Graininess-aware deep feature learning for pedestrian detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision\u2014ECCV 2018, pp. 745\u2013761. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_45"},{"key":"1439_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-020-2969-8","volume":"64","author":"J Xie","year":"2021","unstructured":"Xie, J., Pang, Y., Cholakkal, H., Anwer, R., Khan, F., Shao, L.: PSC-NET: learning part spatial co-occurrence for occluded pedestrian detection. Sci. China Inf. Sci. 64, 1\u201313 (2021). https:\/\/doi.org\/10.1007\/s11432-020-2969-8","journal-title":"Sci. China Inf. Sci."},{"key":"1439_CR10","doi-asserted-by":"publisher","unstructured":"Liu, W., Liao, S., Ren, W., Hu, W., Yu, Y.: High-level semantic feature detection: a new perspective for pedestrian detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5187\u20135196 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00533","DOI":"10.1109\/CVPR.2019.00533"},{"key":"1439_CR11","doi-asserted-by":"publisher","unstructured":"Zhuang, C., Li, Z., Zhu, X., Lei, Z., Li, S.Z.: SADet: learning an efficient and accurate pedestrian detector. In: 2021 IEEE International Joint Conference on Biometrics (IJCB), IEEE, pp. 1\u20138 (2021). https:\/\/doi.org\/10.1109\/IJCB52358.2021.9484371","DOI":"10.1109\/IJCB52358.2021.9484371"},{"key":"1439_CR12","doi-asserted-by":"publisher","unstructured":"Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203\u20133212 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2018.2815688","DOI":"10.1109\/TPAMI.2018.2815688"},{"key":"1439_CR13","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1007\/s10489-021-02496-y","volume":"52","author":"J Wang","year":"2022","unstructured":"Wang, J., Yu, J., He, Z.: DECA: a novel multi-scale efficient channel attention module for object detection in real-life fire images. Appl. Intell. 52, 1362\u20131375 (2022). https:\/\/doi.org\/10.1007\/s10489-021-02496-y","journal-title":"Appl. Intell."},{"key":"1439_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. (2015). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1439_CR15","doi-asserted-by":"publisher","unstructured":"Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., Sun, J.: Thundernet: towards real-time generic object detection on mobile devices. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6718\u20136727 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00682","DOI":"10.1109\/ICCV.2019.00682"},{"key":"1439_CR16","doi-asserted-by":"publisher","unstructured":"Wang, Z., Wu, Z., Lu, J., Zhou, J.: BiDet: an efficient binarized object detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2049\u20132058 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00212","DOI":"10.1109\/CVPR42600.2020.00212"},{"key":"1439_CR17","unstructured":"Cui, Y., Yang, L., Liu, D.: Dynamic proposals for efficient object detection (2022). arXiv:2207.05252"},{"key":"1439_CR18","doi-asserted-by":"publisher","unstructured":"Huang, L., Yang, Y., Deng, Y., Yu, Y.: Densebox: unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509.04874 (2015). https:\/\/doi.org\/10.48550\/arXiv.1509.04874","DOI":"10.48550\/arXiv.1509.04874"},{"key":"1439_CR19","doi-asserted-by":"publisher","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6569\u20136578 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"key":"1439_CR20","doi-asserted-by":"publisher","unstructured":"Liu, Z., Zheng, T., Xu, G., Yang, Z., Liu, H., Cai, D.: Training-time-friendly network for real-time object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11685\u201311692 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6838","DOI":"10.1609\/aaai.v34i07.6838"},{"key":"1439_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108868","volume":"131","author":"H Su","year":"2022","unstructured":"Su, H., He, Y., Jiang, R., Zhang, J., Zou, W., Fan, B.: DSLA: dynamic smooth label assignment for efficient anchor-free object detection. Pattern Recogn. 131, 108868 (2022). https:\/\/doi.org\/10.1016\/j.patcog.2022.108868","journal-title":"Pattern Recogn."},{"key":"1439_CR22","doi-asserted-by":"publisher","unstructured":"Liu, S., Huang, D., Wang, Y.: Adaptive NMS: refining pedestrian detection in a crowd. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6459\u20136468 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00662","DOI":"10.1109\/CVPR.2019.00662"},{"key":"1439_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108605","author":"J Wang","year":"2022","unstructured":"Wang, J., Zhao, C., Huo, Z., Qiao, Y., Sima, H.: High quality proposal features generation for crowded pedestrian detection. Pattern Recogn. (2022). https:\/\/doi.org\/10.1016\/j.patcog.2022.108605","journal-title":"Pattern Recogn."},{"key":"1439_CR24","doi-asserted-by":"publisher","unstructured":"Zhou, K., Chen, L., Cao, X.: Improving multispectral pedestrian detection by addressing modality imbalance problems. In: European Conference on Computer Vision, pp. 787\u2013803. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_46","DOI":"10.1007\/978-3-030-58523-5_46"},{"key":"1439_CR25","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"1439_CR26","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"1439_CR27","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1439_CR28","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s11760-020-01742-z","volume":"15","author":"J Ma","year":"2021","unstructured":"Ma, J., Wan, H., Wang, J., Xia, H., Bai, C.: An improved scheme of deep dilated feature extraction on pedestrian detection. SIViP 15, 231\u2013239 (2021). https:\/\/doi.org\/10.1007\/s11760-020-01742-z","journal-title":"SIViP"},{"key":"1439_CR29","doi-asserted-by":"publisher","unstructured":"Zhang, S., Benenson, R., Schiele, B.: Citypersons: a diverse dataset for pedestrian detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133221 (2017). https:\/\/doi.org\/10.48550\/arXiv.1702.05693","DOI":"10.48550\/arXiv.1702.05693"},{"key":"1439_CR30","doi-asserted-by":"publisher","unstructured":"Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., Sun, J.: Crowdhuman: a benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123 (2018). https:\/\/doi.org\/10.48550\/arXiv.1805.00123","DOI":"10.48550\/arXiv.1805.00123"},{"key":"1439_CR31","doi-asserted-by":"publisher","unstructured":"Liu, W., Liao, S., Hu, W., Liang, X., Chen, X.: Learning efficient single-stage pedestrian detectors by asymptotic localization fitting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 618\u2013634 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_38","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"1439_CR32","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1016\/j.dsp.2021.103311","volume":"18","author":"J Ma","year":"2021","unstructured":"Ma, J., Wan, H., Wang, J., Xia, H., Bai, C.: An improved one-stage pedestrian detection method based on multi-scale attention feature extraction. J. Real-Time Image Proc. 18, 1965\u20131978 (2021). https:\/\/doi.org\/10.1016\/j.dsp.2021.103311","journal-title":"J. Real-Time Image Proc."},{"key":"1439_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2021.103311","volume":"550","author":"Q Li","year":"2021","unstructured":"Li, Q., Qiang, H., Li, J.: Conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection. Inf. Sci. 550, 1\u201312 (2021). https:\/\/doi.org\/10.1016\/j.dsp.2021.103311","journal-title":"Inf. Sci."},{"key":"1439_CR34","doi-asserted-by":"publisher","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.\u00a0Z.: Occlusion-aware r-CNN: detecting pedestrians in a crowd. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 637\u2013653 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_39","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"1439_CR35","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.neucom.2020.03.037","volume":"400","author":"R Lu","year":"2020","unstructured":"Lu, R., Ma, H., Wang, Y.: Semantic head enhanced pedestrian detection in a crowd. Neurocomputing 400, 343\u2013351 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.03.037","journal-title":"Neurocomputing"},{"key":"1439_CR36","doi-asserted-by":"publisher","unstructured":"Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.: Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7774\u20137783 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00811","DOI":"10.1109\/CVPR.2018.00811"},{"key":"1439_CR37","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2020.05.019","volume":"409","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Yang, X., Liu, Y., Xu, C.: Asymmetric multi-stage CNNs for small-scale pedestrian detection. Neurocomputing 409, 12\u201326 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.05.019","journal-title":"Neurocomputing"},{"key":"1439_CR38","doi-asserted-by":"publisher","first-page":"76243","DOI":"10.1109\/ACCESS.2020.2986476","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Yi, P., Zhou, D., Yang, X., Yang, D., Zhang, Q., Wei, X.: CSANet: channel and spatial mixed attention CNN for pedestrian detection. IEEE Access 8, 76243\u201376252 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2986476","journal-title":"IEEE Access"},{"key":"1439_CR39","doi-asserted-by":"publisher","unstructured":"Song, T., Sun, L., Xie, D., Sun, H., Pu, S.: Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 536\u2013551 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_33","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"1439_CR40","doi-asserted-by":"publisher","unstructured":"Wang, Z., Wang, J., Yang, Y.: Resisting the distracting-factors in pedestrian detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020). https:\/\/doi.org\/10.48550\/arXiv.2005.07344","DOI":"10.48550\/arXiv.2005.07344"},{"key":"1439_CR41","doi-asserted-by":"publisher","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9627\u20139636 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"1439_CR42","doi-asserted-by":"publisher","unstructured":"Rukhovich, D., Sofiiuk, K., Galeev, D., Barinova, O., Konushin, A.: IterDet: iterative scheme for object detection in crowded environments. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 344\u2013354. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-73973-7_33","DOI":"10.1007\/978-3-030-73973-7_33"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01439-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01439-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01439-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T08:05:39Z","timestamp":1694246739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01439-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1439"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01439-6","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]},"assertion":[{"value":"28 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"85"}}