Human Action Recognition using 3D CNN

• Video classification problem on spatiotemporal dimensions
• Used concept of Separable Kernels to split 3D CNN into (2+1)D CNN to reduce number of parameters.
• Custom ResNet model with each 2D conv layer replaced by a (2+1)D conv layer
• Achieved test accuracy >70%

Dataset: UCF101 - Action Recognition Data Set

References

[1] D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri, ‘A Closer Look at Spatiotemporal Convolutions for Action Recognition’, arXiv [cs.CV]. 2018.

[2] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, ‘Learning Spatiotemporal Features with 3D Convolutional Networks’, arXiv [cs.CV]. 2015.

[3] C. Feichtenhofer, A. Pinz, and R. P. Wildes, ‘Spatiotemporal Residual Networks for Video Action Recognition’, CoRR, vol. abs/1611.02155, 2016.