Chris Pollett >
Students > [Bio] [Blog] [Rapid Object Detection using a Boosted Cascade of Simple Features-PDF] |
Deliverable 4Implementing Demo2Vec modelGoal: To recreate Demo2Vec model on OPRA dataset as discussed in Deliverable 3. Aim: To get insight on how to perform affordance learning on videos and create baseline model for the project Conclusion: Created partial Demo2Vec. Implemented ConvLSTM to handle time series data. Utilized human activity recognition datasets to implement neural net. I experimented and created neural network to perform human activity recognition on UCF 101- Action Recognition Dataset Dataset: From UCF 101 dataset, I chose 5 human activity.Biking Bench Press Archery Baby Crawling BasketBall Model Generation: 1. I utilized VVG-16 model with pretrained weights restored from imageNet dataset as our base model. 2. For videos, I implemented a ConvLSTM network to handle time series data and extract spatial-temporal information from the input. 3. All the videos were subsampled at a rate of 5 frames per second. All the generated frames were resized to 112*112. Rest of the implementation will be carried out in the next semester Code to generate Frames Base Model Model Summary |