Students: Meera & Si Mentor: Afshin Dehghan WEEK 4: DEEP TRACKING.

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Presentation transcript:

Students: Meera & Si Mentor: 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), 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

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

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 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.

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