نفیسه دهقان پور دفتر علم سنجی تلفن: 3912563 [email protected]
Students: Meera & Si Mentor: Afshin Dehghan WEEK 4: DEEP TRACKING.
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Transcript of Students: Meera & Si Mentor: Afshin Dehghan WEEK 4: DEEP TRACKING.
Students: Meera & SiMentor: Afshin Dehghan
WEEK 4:DEEP TRACKING
CURRENT PROGRESS
HANDCRAFTED FEATURES VS AUTOENCODERS
1 Online Object Tracking: A Benchmark. Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, “Online Object Tracking: A Benchmark,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
Downloaded 10 videos from
Online Object Tracking: A
Benchmark1, and cut them to 115
frames
Compared autoencoder
results with HOG and Color Histogram
HOG performed the worst overall
Autoencoders performed the best in all but 2
videos
VISUALIZING THE FILTERS
Currently running code with 3 layers
Could not visualize the 2nd and 3rd layersCould visualize the 1st layer
2nd and 3rd layer representations are different
from the 1st
The current visualization function does not apply to
them
APPLYING GAUSSIAN CONFIDENCE BASED ON MOTION VECTOR
Performance for the sequences was
improved
Change in confidence values
We observed the effect of a Gaussian motion
model
NEXT STEPS
Training 2
• Finish downloading 1 million images of same size
• Pre-train network with the images• Fine tune the network
Visualizing Layers
• Currently we can only visualize the 1st layer of filters
• Do more research and implement a method to visualize 2nd and 3rd layers
NEXT STEPS
2 Lamblin, Pascal and Yoshua Bengio. Important Gains from Supervised Fine-Tuning of Deep Architectures on Large Labeled Sets. NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop.
Variations
• Patch size• Greyscale images
Further
Reading
• Fine tuning networks• Visualizing filters of higher layers• Learning motion: provide temporal data to
network so it can learn the vector
NEXT STEPS