Learning Padless Correlation Filters for Boundary-effect Free Tracking
Recently, discriminative correlation filters (DCFs) have achieved enormous popularity in the tracking community due to high accuracy and beyond real-time speed. Among different DCF variants, spatially regularized discriminative correlation filters (SRDCFs) demonstrate excellent performance in suppressing boundary effects induced from circularly shifted training samples. However, SRDCF have two drawbacks which may be the bottlenecks for further performance improvement. First, SRDCF needs to...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Sensors Journal|
|Access Rights:||Open Access|
|01_Li_Learning_Padless_Correlation_2018.pdf||3.04 MB||Adobe PDF|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.