DocumentCode :
3745932
Title :
Convolutional Features for Correlation Filter Based Visual Tracking
Author :
Martin Danelljan; H?ger;Fahad Shahbaz Khan;Michael Felsberg
Author_Institution :
Comput. Vision Lab., Linkoping Univ., Linkoping, Sweden
fYear :
2015
Firstpage :
621
Lastpage :
629
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.
Keywords :
"Target tracking","Correlation","Feature extraction","Visualization","Standards","Convolution","Benchmark testing"
Publisher :
ieee
Conference_Titel :
Computer Vision Workshop (ICCVW), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/ICCVW.2015.84
Filename :
7406433
Link To Document :
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