Improving of Action Localization in Videos Using the Novel Feature Extraction

Nowadays, automatic video analysis using state-of-the-art machine learning techniques is highly relevant and finds lots of industrial applications. This approach gives the possibility to solve a set of the problems such as finding the main part of the video, tracking in time by the duration of an action that is labor-intensive and almost impossible to be solved manually or by old-fashioned methods. One of the most promising approaches is the Weakly-supervised method. The Weakly-supervised technique can be used for temporal action localization, which is a challenging task because it uses only video-level labels during the training. The main benefit of this method is that it is not required to create large-scale datasets with temporal annotations, which are quite time-consuming. In this work, we propose to improve the current state-of-the-art in temporal action localization by introducing the new feature extraction procedure. We achieved better results on the RGB stream by making the new feature extraction using more powerful neural network models on the benchmark THUMOS”14 dataset [1]. We also provide a deep analysis of the importance of the feature extraction procedure in the whole workflow for temporal action detection. The results of our investigations can be applied directly for solving a wide range of industrial problems in a robust and accurate manner.