Energy based fast event retrieval in video with temporal match kernel

We propose a fast event retrieval method in video databases based on temporal match kernel. The basic idea of the baseline is to maximize the score function for all possible relative timestamps. However, considering the stability and computational cost, we simplify the similarity score by calculating the energy of the score function. In this way, maximizing the score function which is time-consuming and sensitive to noise is avoided. We derive the simplified energy formulation by using Parseval's theorem, which describes the unitarity of Fourier transform. Further, by our formulation, the problem becomes a simple nearest neighbor search, and we can use product quantization (PQ) to accelerate the computation. We evaluate our method on EVVE dataset for event retrieval. The experimental results show that our approach is much faster than the baseline, with a higher mAP as well. Comparison with state of the art demonstrates the efficacy of our approach.