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[[File:Track_Results.webm|thumb|An example of object tracking]]
Small object detection has applications in various fields such as Video [[surveillance]] (Traffic video Surveillance,<ref>{{Cite book |last1=Saran K B |last2=Sreelekha G |title=2015 International Conference on Control Communication & Computing India (ICCC) |chapter=Traffic video surveillance: Vehicle detection and classification |chapter-url=https://ieeexplore.ieee.org/document/7432948 |year=2015 |___location=Trivandrum, Kerala, India |publisher=IEEE |pages=516–521 |doi=10.1109/ICCC.2015.7432948 |isbn=978-1-4673-7349-4|s2cid=14779393 }}</ref><ref>{{Cite journal |last=Nemade |first=Bhushan |date=2016-01-01 |title=Automatic Traffic Surveillance Using Video Tracking |url=https://www.sciencedirect.com/science/article/pii/S1877050916001836 |journal=Procedia Computer Science |series=Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV) 2016 |language=en |volume=79 |pages=402–409 |doi=10.1016/j.procs.2016.03.052 |issn=1877-0509}}</ref> [[Content-based image retrieval|Small object retrieval]],<ref>{{Cite journalbook |last1=Guo |first1=Haiyun |last2=Wang |first2=Jinqiao |last3=Xu |first3=Min |last4=Zha |first4=Zheng-Jun |last5=Lu |first5=Hanqing |datetitle=2015-10-13Proceedings of the 23rd ACM international conference on Multimedia |titlechapter=Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios |date=2015-10-13 |chapter-url=https://doi.org/10.1145/2733373.2806349 |journal=Proceedings of the 23rd ACM International Conference on Multimedia |series=MM '15 |___location=New York, NY, USA |publisher=Association for Computing Machinery |pages=859–862 |doi=10.1145/2733373.2806349 |isbn=978-1-4503-3459-4|s2cid=9041849 }}</ref><ref>{{Cite journal |last1=Galiyawala |first1=Hiren |last2=Raval |first2=Mehul S. |last3=Patel |first3=Meet |date=2022-05-20 |title=Person retrieval in surveillance videos using attribute recognition |url=https://doi.org/10.1007/s12652-022-03891-0 |journal=Journal of Ambient Intelligence and Humanized Computing |language=en |doi=10.1007/s12652-022-03891-0 |s2cid=248951090 |issn=1868-5145}}</ref> [[Anomaly detection]],<ref>{{Cite journal |last1=Ingle |first1=Palash Yuvraj |last2=Kim |first2=Young-Gab |date=2022-05-19 |title=Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities |journal=Sensors |language=en |volume=22 |issue=10 |pages=3862 |doi=10.3390/s22103862 |issn=1424-8220 |pmc=9143895 |pmid=35632270|bibcode=2022Senso..22.3862I |doi-access=free }}</ref> [[Maritime surveillance]], [[Aerial survey|Drone surveying]], [[Traffic flow|Traffic flow analysis]],<ref>{{Cite journal |last1=Tsuboi |first1=Tsutomu |last2=Yoshikawa |first2=Noriaki |date=2020-03-01 |title=Traffic flow analysis in Ahmedabad (India) |url=https://www.sciencedirect.com/science/article/pii/S2213624X18301974 |journal=Case Studies on Transport Policy |language=en |volume=8 |issue=1 |pages=215–228 |doi=10.1016/j.cstp.2019.06.001 |s2cid=195543435 |issn=2213-624X}}</ref> and [[Video tracking|Object tracking]].
== Problems with small objects ==
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=== Well-Optimised techniques for small object detection ===
Various deep learning techniques are available that focus on such object detection problems: e.g., Feature-Fused SSD,<ref>{{Cite journalbook |last1=Cao |first1=Guimei |last2=Xie |first2=Xuemei |last3=Yang |first3=Wenzhe |last4=Liao |first4=Quan |last5=Shi |first5=Guangming |last6=Wu |first6=Jinjian |title=Ninth International Conference on Graphic and Image Processing (ICGIP 2017) |chapter=Feature-fused SSD: Fast detection for small objects |editor-first1=Junyu |editor-first2=Hui |editor-last1=Dong |editor-last2=Yu |date=2018-04-10 |title=Featurechapter-fused SSD: fast detection for small objects |url=https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10615/106151E/Feature-fused-SSD-fast-detection-for-small-objects/10.1117/12.2304811.full |journal=Ninth International Conference on Graphic and Image Processing (ICGIP 2017) |publisher=SPIE |volume=10615 |pages=381–388 |doi=10.1117/12.2304811|arxiv=1709.05054 |bibcode=2018SPIE10615E..1EC |isbn=9781510617414 |s2cid=20592770 }}</ref> YOLO-Z.<ref>{{cite arXiv |last1=Benjumea |first1=Aduen |last2=Teeti |first2=Izzeddin |last3=Cuzzolin |first3=Fabio |last4=Bradley |first4=Andrew |date=2021-12-23 |title=YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles |class=cs.CV |eprint=2112.11798}}</ref> Such methods work on "How to sustain features of small objects while they pass through convolution networks."
