Small object detection: Difference between revisions

Revision as of 01:24, 15 September 2022

Revision as of 04:49, 15 September 2022

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{{Short description|Detecting small objects in digital images}}

'''Small object detection''' is a particular case of [[object detection]] where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as [[Aerial photography|aerial imagery]], [[State of the art|state-of-the-art]] object detection techniques unperformedunder performed because of small objects.

== Uses ==

[[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 journal |last=Saran K B |last2=Sreelekha G |title=Traffic video surveillance: Vehicle detection and classification |url=http://ieeexplore.ieee.org/document/7432948/ |journal=2015 International Conference on Control Communication & Computing India (ICCC) |___location=Trivandrum, Kerala, India |publisher=IEEE |pages=516–521 |doi=10.1109/ICCC.2015.7432948 |isbn=978-1-4673-7349-4}}</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 journal |last=Guo |first=Haiyun |last2=Wang |first2=Jinqiao |last3=Xu |first3=Min |last4=Zha |first4=Zheng-Jun |last5=Lu |first5=Hanqing |date=2015-10-13 |title=Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios |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}}</ref><ref>{{Cite journal |last=Galiyawala |first=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 |issn=1868-5145}}</ref> [[Anomaly detection]],<ref>{{Cite journal |last=Ingle |first=Palash Yuvraj |last2=Kim |first2=Young-Gab |date=2022-05-19 |title=Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities |url=https://www.mdpi.com/1424-8220/22/10/3862 |journal=Sensors |language=en |volume=22 |issue=10 |pages=3862 |doi=10.3390/s22103862 |issn=1424-8220 |pmc=9143895 |pmid=35632270}}</ref> [[Maritime surveillance]], [[Aerial survey|Drone surveying]], [[Traffic flow|Traffic flow analysis]],<ref>{{Cite journal |last=Tsuboi |first=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 |issn=2213-624X}}</ref> and [[Video tracking|Object tracking]].

== Problems with small objects ==

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* At present, [[Unmanned aerial vehicle|drones]] are very widely used in aerial imagery.<ref>{{Cite journal |title=Interactive workshop "How drones are changing the world we live in" |url=http://ieeexplore.ieee.org/document/7486437/ |journal=2016 Integrated Communications Navigation and Surveillance (ICNS) |___location=Herndon, VA |publisher=IEEE |pages=1–17 |doi=10.1109/ICNSURV.2016.7486437 |isbn=978-1-5090-2149-9}}</ref> They are equipped with hardware ([[Sensor|sensors]]) and software ([[Algorithm|algorithms]]) that help maintain a particular stable position during their flight. In windy conditions, the drone automatically makes fine moves to maintain its position and that changes the view near the boundary. It may be possible that some new objects appear near the image boundary. Overall, these affect classification, detection, and eventually tracking accuracy.

[[File:Disp_shadow.jpg|thumb|Shadow and drone movement effect|alt=Here, both images are from same video. See, How the shadow of objects affecting detection accuracy. Also, drone's self-movement changes the scene near boundary(Refer to object "car" at bottom-left corner). ]]

== Methods ==