Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network  

作　　者：Xuan Zhou Jianping Yi 

机构地区：[1]School of Mechanical&Electrical Engineering,Xi’an Traffic Engineering Institute,Xi’an,710300,China [2]School of Electronics and Information,Xi’an Polytechnic University,Xi’an,710048,China

出　　处：《Intelligent Automation & Soft Computing》2023年第8期2103-2116,共14页智能自动化与软计算（英文）

基　　金：supported by the research team of Xi’an Traffic Engineering Institute and the Young and middle-aged fund project of Xi’an Traffic Engineering Institute (2022KY-02).

摘　　要：Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.

关 键 词：Fine-grained action recognition temporal pyramid excitation module temporal receptive multi-excitation module 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]