{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:14Z","timestamp":1760241974490,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,26]],"date-time":"2018-11-26T00:00:00Z","timestamp":1543190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program Of China","award":["2017YFC0806005"],"award-info":[{"award-number":["2017YFC0806005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>At present, the problem of pedestrian detection has attracted increasing attention in the field of computer vision. The faster regions with convolutional neural network features (Faster R-CNN) are regarded as one of the most important techniques for studying this problem. However, the detection capability of the model trained by faster R-CNN is susceptible to the diversity of pedestrians\u2019 appearance and the light intensity in specific scenarios, such as in a subway, which can lead to the decline in recognition rate and the offset of target selection for pedestrians. In this paper, we propose the modified faster R-CNN method with automatic color enhancement (ACE), which can improve sample contrast by calculating the relative light and dark relationship to correct the final pixel value. In addition, a calibration method based on sample categories reduction is presented to accurately locate the target for detection. Then, we choose the faster R-CNN target detection framework on the experimental dataset. Finally, the effectiveness of this method is verified with the actual data sample collected from the subway passenger monitoring video.<\/jats:p>","DOI":"10.3390\/a11120192","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T03:31:33Z","timestamp":1543289493000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement"],"prefix":"10.3390","volume":"11","author":[{"given":"Hongquan","family":"Qu","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meihan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changnian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, North China University of Technology, Shijingshan District, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Wei","sequence":"additional","affiliation":[{"name":"Beijing Urban Construction Design &amp; Development Group Co., Ltd., Beijing 100037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,26]]},"reference":[{"key":"ref_1","unstructured":"Zhang, L. (2006). Safety Problems and Countermeasures of Subway Peak Passenger Flow, Urban Rail Transit Key Technology."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.proeng.2014.04.085","article-title":"A Study on Theoretical Calculation Method of Subway Safety Evacuation","volume":"71","author":"Zhang","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.4028\/www.scientific.net\/AMR.671-674.2965","article-title":"Study on safety evacuation time for passengers in subway station and its application","volume":"671\u2013674","author":"Zhou","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_4","first-page":"96","article-title":"Automatic detection technology of passenger density in Beijing Metro","volume":"4","author":"Zhang","year":"2017","journal-title":"China Railw."},{"key":"ref_5","first-page":"122","article-title":"A Method of Automatic Pedestrian Counting in Metro Station Based on Machine Vision","volume":"30","author":"Chen","year":"2013","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_6","first-page":"45","article-title":"Comparative Research on Algorithm of Passenger Flow Statistics System Based on Intelligent Video Technology","volume":"6","author":"Han","year":"2016","journal-title":"Information Technology"},{"key":"ref_7","first-page":"76","article-title":"Human body detection model based on haar-HOG algorithm","volume":"32","author":"Zhang","year":"2017","journal-title":"Revista de la Facultad de Ingenieria"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yi, Z., and Xue, J. (2014, January 28\u201329). Improving Hog descriptor accuracy using non-linear multi-scale space in people detection. Proceedings of the 2014 ACM Southeast Regional Conference, ACM SE 2014, Kennesaw, GA, USA.","DOI":"10.1145\/2638404.2638468"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huo, Z., Xia, Y., and Zhang, B. (2016, January 15\u201317). Vehicle type classification and attribute prediction using multi-task RCNN. Proceedings of the BioMedical Engineering and Informatics, CISP-BMEI 2016, Datong, China.","DOI":"10.1109\/CISP-BMEI.2016.7852774"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2015, Washington, DC, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Learned-Miller, E. (June, January 30). Face Detection with the Faster R-CNN. Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.82"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Byeon, Y.H., and Kwak, K.C. (2017, January 9\u201313). A Performance Comparison of Pedestrian Detection Using Faster RCNN and ACF. Proceedings of the 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Japan.","DOI":"10.1109\/IIAI-AAI.2017.196"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, W., Zhang, Y., Gulliver, T.A., Chang, S., and Feng, Z. (2016, January 18\u201321). A faster RCNN-based pedestrian detection system. Proceedings of the IEEE Vehicular Technology Conference, Montreal, QC, Canada.","DOI":"10.1109\/VTCFall.2016.7880852"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Roh, M.C., and Lee, J.Y. (2017, January 8\u201312). Refining faster-RCNN for accurate object detection. Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017, Nagoya, Japan.","DOI":"10.23919\/MVA.2017.7986913"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lokanath, M., Kumar, K.S., and Keerthi, E.S. (2017). Accurate object classification and detection by faster-RCNN. IOP Conference Series: Materials Science and Engineering, IOP Publishing.","DOI":"10.1088\/1757-899X\/263\/5\/052028"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive Histogram Equalization and Its Variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zeng, Y.-C. (July, January 28). Automatic local contrast enhancement using adaptive histogram adjustment. Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, Hilton Cancun, Mexico.","DOI":"10.1109\/ICME.2009.5202745"},{"key":"ref_19","first-page":"1052","article-title":"Optimized algorithm for adaptive histogram adjustment","volume":"3338","author":"Gillespy","year":"1998","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/S0146-664X(77)80011-7","article-title":"Image enhancement by histogram transformation","volume":"6","author":"Hummel","year":"1977","journal-title":"Comput. Graph. Image Process."},{"key":"ref_21","unstructured":"Gatta, C., Rizzi, A., and Marini, D. (2002, January 2\u20135). ACE: An Automatic Color Equalization Algorithm. Proceedings of the Conference on Color in Graphics, Imaging, and Vision, CGIV 2002 Final Program and Proceedings, Poitiers, France."},{"key":"ref_22","unstructured":"Korpi-Anttila, J. (1999, January 1). Automatic color enhancement and scene change detection of moving pictures. Proceedings of the Final Program and Proceedings\u2014IS and T\/SID Color Imaging Conference, Scottsdale, AZ, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Choudhury, A., and Medioni, G. (October, January 27). Perceptually motivated automatic color contrast enhancement. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457513"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"266","DOI":"10.5201\/ipol.2012.g-ace","article-title":"Automatic Color Enhancement (ACE) and its Fast Implementation","volume":"2","author":"Getreuer","year":"2012","journal-title":"Image Process. Line"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast Feature Pyramids for Object Detection","volume":"36","author":"Appel","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, D., Ouyang, W., Ricci, E., Wang, X., and Sebe, N. (2017, January 21\u201326). Learning Cross-Modal Deep Representations for Robust Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.451"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Zhou, H., Li, H., Li, Q., Yan, J., and Wang, X. (2017). Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI, accepted.","DOI":"10.1109\/TPAMI.2017.2738645"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Zeng, X., and Wang, X. (2015). Partial Occlusion Handling in Pedestrian Detection with a Deep Model. IEEE Trans. Circuits Syst. Video Technol. TCSVT, accepted.","DOI":"10.1109\/TCSVT.2015.2501940"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/11\/12\/192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:32:06Z","timestamp":1760196726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/11\/12\/192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,26]]},"references-count":28,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["a11120192"],"URL":"https:\/\/doi.org\/10.3390\/a11120192","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2018,11,26]]}}}