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Automated Video Cutting: Deep Learning Lecture Capture Variable lecture lengths Guest lecturers Staff required to cut videos daily Students want videos asap

Automated Video Cutting: Deep Learning Using Caffe to classify snapshots from video 1 image every 30 second Caffenet model using 5 layers of convolution followed by 3 layers of neurons CPU mode was fairly slow ( ~750 inter/sec) Enable GPU mode 5-10x faster Trained for 190,000 iterations (~26hrs)

Automated Video Cutting: Deep Learning Ubuntu installation documentation was for 14.04 discrepancies with 15.10 Getting CUDA installed to allow GPU mode Python interface was giving drastically different results. Ended up using caffe cli tool in testing mode.

Automated Video Cutting: Deep Learning Validation set, random subset of 1500 images Accuracy of 99.8% Testing set from week of footage following training: Accuracy of raw net: 84.4% Accuracy of sliding window: 88.1%

Automated Video Cutting: Deep Learning

Automated Video Cutting: Deep Learning