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Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Published on Oct 24, 20162272 Views

Martin Danelljan

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by i

ECCV 2016 - Amsterdam

Related categories

Computer Vision

Chapter list

Learning Continuous Convolution Operators for Visual Tracking00:00
“tracking itself is by and large a solved problem”00:01
Visual Object Tracking (VOT) 2016 challenge results - 100:19
Visual Object Tracking (VOT) 2016 challenge results - 200:30
Visual Object Tracking (VOT) 2016 challenge results - 300:37
Tracking Challenges00:54
Feature Point Tracking01:17
Discriminative Correlation Filters (DCF) - 102:05
Discriminative Correlation Filters (DCF) - 202:23
Discriminative Correlation Filters (DCF) - 302:29
Discriminative Correlation Filters (DCF) - 402:39
Discriminative Correlation Filters (DCF) - 502:43
Discriminative Correlation Filters (DCF) - 602:59
Our Approach: Overview - 103:14
Our Approach: Overview - 203:32
Our Approach: Overview - 303:40
Our Approach: Overview - 403:54
Interpolation Operator - 104:13
Interpolation Operator - 204:28
Interpolation Operator - 304:33
Interpolation Operator - 404:39
Interpolation Operator - 504:42
Interpolation Operator - 604:49
Convolution Operator - 105:01
Convolution Operator - 205:07
Convolution Operator - 305:11
Convolution Operator - 405:19
Convolution Operator - 505:29
Training Loss - 105:37
Training Loss - 205:42
Training Loss - 305:46
Training Loss - 406:09
Localization - 106:35
Localization - 206:45
Localization - 306:56
Localization - 407:05
Localization - 507:08
Localization - 607:15
Object Tracking Framework - 107:22
Object Tracking Framework - 207:43
Object Tracking Framework - 307:50
Experiments: Object Tracking - 207:57
Experiments: Object Tracking - 308:25
OTB Dataset (100 videos)08:56
Temple-Color Dataset (128 videos)09:13
Visual Object Tracking Challenge 2016 09:20
Feature Point Tracking Framework - 109:38
Feature Point Tracking Framework - 209:53
Feature Point Tracking Framework - 310:04
Conclusions10:24
Thank You10:58