关键词: 轨迹, 注意力机制, 引导感知学习, 动作连贯性, 动作质量评估

Abstract: Action quality assessment has attracted many researchers as a challenging visual task. Current research methods mainly focus on improving the feature extraction capability of backbone networks, ignoring the effect of motion trajectories. However, the consistency of the movement is also an important factor for evaluating the execution of the movement in real competitions. In this paper we design a novel trajectory-guided perceptual learning framework for action quality assessment, which uses trajectory descriptors to guide the model to perceptually learn action coherence information, and to achieve interactive learning between different information. Aiming at the lack of trajectory labels in the current dataset, we design an unsupervised optical flow trajectory extraction method based on the Farneback optical flow method to obtain motion trajectory information, and use the acquired optical flow trajectory features as cue words to guide the model to learn the video features perceptually. Finally a learnable spline curves of the KAN network is used to fit the data distribution of the mixture of features, so as to establish a more accurate mapping relationship. We conduct experiments on MTL-AQA, AQA-7, FineDiving and JIGSAWS datasets, and the spearman rank correlation coefficients on the above datasets can reach 0.9101 (MTL-AQA), 0.912 (AQA-7), 0.882 (FineDiving), and 0.98 (JIGSAWS), respectively.

Key words: trajectory, attention mechanism, guided perception learning, consistency of action, action quality assessment