Temporal Action Localization Using Gated Recurrent Units

Temporal Action Localization (TAL) task in which the aim is to predict the start and end of each action and its class label has many applications in the real world. But due to its complexity, researchers have not reached great results compared to the action recognition task. The complexity is related to predicting precise start and end times for different actions in any video. In this paper, we propose a new network based on Gated Recurrent Unit (GRU) and two novel post-processing ideas for TAL task. Specifically, we propose a new design for the output layer of the GRU resulting in the so-called GRU-Splitted model. Moreover, linear interpolation is used to generate the action proposals with precise start and end times. Finally, to rank the generated proposals appropriately, we use a Learn to Rank (LTR) approach. We evaluated the performance of the proposed method on Thumos14 dataset. Results show the superiority of the performance of the proposed method compared to state-of-the-art. Especially in the mean Average Precision (mAP) metric at Intersection over Union (IoU) 0.7, we get 27.52% which is 5.12% better than that of state-of-the-art methods.