Week 8
Students: Meera Hahn and Si Chen
Mentor: Afshin Deghan
Cifar Network
• 3 convolutional layer network
• Trained Caffe classifier using this network and our
training images from the first frames of the video
sequence
• Experimented with parameters such as number
of training iterations, learning rate and number of
training images
Imagenet Network
• 5 convolutional layers
• This is the network we ran to train the offline Caffe
tracker with Caffe’s given pre-trained weights
which gave very high results
• Trained Caffe classifier using this network and our
training images from the first frames of the video
sequence
F Score Comparisons
• Running Offline Caffe Deep Tracking code on all
50 sequences
• Fixed: processing greyscale images
• Calculated STRUCK results from benchmark
results
Autoencoder Fully Connected Offline CAFFE
STRUCK
+ SVM
Network
Deep Tracker
115
Full
62.84
45.14
77.01
75.57
61.24
56.28
Online Object Tracking: A
Benchmark 1
Spatial Robustness
Evaluation (SRE)
• 8 spatial shifts of the ground
truth = 12 total shifts
Precision Plot
• Error in center location
Temporal Robustness
Evaluation (TRE)
• Frames evaluated based
on bounding box location
Success Plot
• Overlap in bounding boxes
• Ratio of successful tracking
Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, “Online Object Tracking: A
Benchmark,” in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2013.
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