资源论文Recognizing Complex Events Using Large Margin Joint Low-Level Event Model

Recognizing Complex Events Using Large Margin Joint Low-Level Event Model

2020-04-02 | |  50 |   44 |   0

Abstract

In this paper we address the challenging problem of complex event recognition by using low-level events. In this problem, each com- plex event is captured by a long video in which several low-level events happen. The dataset contains several videos and due to the large num- ber of videos and complexity of the events, the available annotation for the low-level events is very noisy which makes the detection task even more challenging. To tackle these problems we model the joint relation- ship between the low-level events in a graph where we consider a node for each low-level event and whenever there is a correlation between two low-level events the graph has an edge between the corresponding nodes. In addition, for decreasing the effect of weak and/or irrelevant low-level event detectors we consider the presence/absence of low-level events as hidden variables and learn a discriminative model by using latent SVM formulation. Using our learned model for the complex event recognition, we can also apply it for improving the detection of the low-level events in video clips which enables us to discover a conceptual description of the video. Thus our model can do complex event recognition and explain a video in terms of low-level events in a single framework. We have eval- uated our proposed method over the most challenging multimedia event detection dataset. The experimental results reveals that the proposed method performs well compared to the baseline method. Further, our re- sults of conceptual description of video shows that our model is learned quite well to handle the noisy annotation and surpass the low-level event detectors which are directly trained on the raw features.

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