Paper Info
Title
Convolutional Features for Correlation Filter Based Visual Tracking
Abstract
Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they miti-gate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard hand-crafted features. Finally, results comparable to state-of-the-art trackers are obtained on all three benchmark datasets.
Year
DOI
Venue
2015
10.1109/ICCVW.2015.84
ICCV Workshops
Keywords
Field
DocType
convolutional features,visual object tracking,computer vision problem,convolutional CNN layer,discriminative correlation filter-based visual tracking framework,structural information,low-dimensionality activation,OTB benchmark dataset,ALOV300++ benchmark dataset,image classification
Computer vision,Pattern recognition,Computer science,Visualization,Curse of dimensionality,Feature extraction,Eye tracking,Video tracking,Artificial intelligence,Contextual image classification,Discriminative model,Benchmark (computing)
Conference
Citations 
PageRank 
References 
100
1.96
30
Authors
4
Name
Order
Citations
PageRank
Danelljan Martin1134449.35
Gustav Häger259614.86
Fahad Shahbaz Khan3162269.24
Michael Felsberg42419130.29