A tensor motion descriptor based on histograms of gradients and optical flow

• This paper presents a new tensor motion descriptor using only optical flow and HOG3D.
• No local features are extracted and no bag-of-features strategy is used.
• The descriptor is evaluated by classifying KTH, UCF11 and Hollywood2 datasets.
• Our method achieves competitive recognition rates in all datasets.

This paper presents a new tensor motion descriptor only using optical flow and HOG3D information: no interest points are extracted and it is not based on a visual dictionary. We propose a new aggregation technique based on tensors. This is a double aggregation of tensor descriptors. The first one represents motion by using polynomial coefficients which approximates the optical flow. The other represents the accumulated data of all histograms of gradients of the video. The descriptor is evaluated by a classification of KTH, UCF11 and Hollywood2 datasets, using a SVM classifier. Our method reaches 93.2% of recognition rate with KTH, comparable to the best local approaches. For the UCF11 and Hollywood2 datasets, our recognition achieves fairly competitive results compared to local and learning based approaches.