Kernelized Covariance for Action Recognition


The descriptive power of the covariance matrix is limited in capturing linear mutual dependencies between variables only. To solve this issue, we present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. Our proposed encoding (Kernelized-COV) generalizes the original covariance representation without compromising the efficiency of the computation. Despite its broad generability, the aforementioned paper applied Kernelized-COV to 3D action recognition from MoCap data.



[Code available]

If you use our code, please cite our paper as:

  title={Kernelized Covariance for Action Recognition},
  author={Cavazza, Jacopo and Zunino, Andrea and San Biagio, Marco and Murino, Vittorio},
  booktitle={23rd International Conference on Pattern Recognition (ICPR'16)},
month={December}, organization={IEEE} }

For additional information, please contact: Jacopo Cavazza



  • J. Cavazza, A. Zunino, M. San Biagio and V. Murino
    "Kernelized Covariance for Action Recognition"
    International Conference on Pattern Recognition, (ICPR), 2016 [PDF]