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Published byDarcy Bishop Modified over 9 years ago
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Students: Meera & Si Mentor: Afshin Dehghan WEEK 4: DEEP TRACKING
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CURRENT PROGRESS
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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 Benchmark 1, 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
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VISUALIZING THE FILTERS Currently running code with 3 layers Could not visualize the 2 nd and 3 rd layers Could visualize the 1 st layer 2 nd and 3 rd layer representations are different from the 1st The current visualization function does not apply to them
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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
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NEXT STEPS
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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 1 st layer of filters Do more research and implement a method to visualize 2 nd and 3 rd 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.
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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
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