Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3755
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDeshmukh, Prashant-
dc.contributor.authorKadha, Vijayakumar-
dc.contributor.authorRayasam, Krishna Chaitanya-
dc.contributor.authorDas, Santos Kumar-
dc.date.accessioned2022-10-20T04:44:31Z-
dc.date.available2022-10-20T04:44:31Z-
dc.date.issued2022-09-
dc.identifier.citationInternational Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2022) ,September 23-25, 2022 at BITS Goa Campus.en_US
dc.identifier.urihttp://hdl.handle.net/2080/3755-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractTraffic camera video feeds are helpful in implementing intelligent vehicle detection and classification (IVDC). It has various applications in the transportation engineering domain, such as queue length estimation, vehicle tracking, traffic parameters estimation etc. However, in the Indian traffic wide variety of vehicles (motorbikes, auto-rickshaws, cycle-rickshaws, minitrucks, trucks etc.) travel on the road. They do not follow lane disciplined and occluded each other, making vehicle detection very challenging. This work presented an anchor-free object detection model (YOLOX) on the Indian traffic dataset (ITD) and compared it with the existing object detection models. It achieves 88% mean average precision (mAP) and 37 frames per second (FPS) on ITD.en_US
dc.subjectIndian trafficen_US
dc.subjectvehicle detectionen_US
dc.subjectdeep learningen_US
dc.subjectanchor-free object detectionen_US
dc.titleVehicle detection in Indian traffic using an anchor-free object detectoren_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
DasS_SCRC2022.pdf10.63 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.