A Tensor Motion Descriptor Based on Multiple Gradient Estimators

This work presents a novel approach for motion description in videos using multiple band-pass filters which act as first order derivative estimators. The filters response on each frame are coded into individual histograms of gradients to reduce their dimensionality. They are combined using orientation tensors. No local features are extracted and no learning is performed, i.e., the descriptor depends uniquely on the input video. Motion description can be enhanced even using multiple filters with similar or overlapping frequency response. For the problem of human action recognition using the KTH database, our descriptor achieved the recognition rate of 93.3% using three Daubechies filters, one extra filter designed to correlate them, two-fold protocol and a SVM classifier. It is superior to most global descriptor approaches and fairly comparable to the state-of-the-art methods.