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Week 4: Deep tracking

Week 4: Deep tracking. Students: Meera & Si Mentor: Afshin Dehghan. Current progress. Handcrafted features VS Autoencoders. Downloaded 10 videos from Online Object Tracking: A Benchmark 1 , and cut them to 115 frames. Compared autoencoder results with HOG and Color Histogram.

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Week 4: Deep tracking

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  1. Week 4:Deep tracking Students: Meera & Si Mentor: AfshinDehghan

  2. Current progress

  3. Handcrafted features VS Autoencoders • Downloaded 10 videos from Online Object Tracking: A Benchmark1, and cut them to 115 frames • Compared autoencoder results with HOG and Color Histogram Autoencoders performed the best in all but 2 videos • HOG performed the worst overall 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.

  4. Visualizing the filters

  5. Change in confidence values applying Gaussian confidence based on motion vector • We observed the effect of a Gaussian motion model • Performance for the sequences was improved

  6. Next steps

  7. Next Steps 2Lamblin, Pascal and YoshuaBengio. Important Gains from Supervised Fine-Tuning of Deep Architectures on Large Labeled Sets. NIPS 2010 Deep Learning and Unsupervised Feature Learning Workshop.

  8. Next Steps

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