1. Object Classification through Deconvolutional Neural Networks
Student: Carlos Rubiano
Mentor: Oliver Nina

2. ChaLearn Looking at People
Action/interaction spotting on RGB data
Recognize actions using 235 performances of 11 action classes recorded and manually labeled in continuous RGB sequences of people performing natural isolated and collaborative behaviors

4. Video Classification with CNN
Use CNN structure to classify actions in videos
Using similar network that classifies CIFAR and takes inputs of 32 x 32 x 3
Also do this using ImageNet network and takes inputs of 256 x 256 x 3
Extract the frames from the videos
Applied optical flow on the frames of the videos
Resized the data for faster performance, and match inputs of the network
Optical flow gives temporal information across two frames

5. ChaLearn Looking at People Dataset
Optical Flow
Optical Flow

6. Model

7. Results from ChaLearn Looking at People 2014
With automatic segmentation

8. Results on Validation Set
Spatial DCNN
Stacks
Randomly Initialized
Pretrained with Fine tuning
1 frame
0.53
0.57
3 frames
0.55
5 frames
0.6
Temporal DCNN
Stacks
Randomly Initialized
Pretrained with Fine tuning
1 frame
0.58
0.5
3 frames
0.54 -
5 frames
With manual segmentation

9. Also…
Add: - motion boundary histograms - Dense SIFT - HOG