# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 1 Introduction  
Video human action recognition and analysis is one key research topic pertaining to applications including healthcare monitoring, human-computer interaction, gait analysis, and security surveillance [1, 2, 3, 4]. For example, in healthcare settings, recognizing human activities allows for understanding the motion patterns and habits of elderly individuals. Thus, timely emergency responses, through video action recognition, will contribute to the creation of safer and healthier living environments for communities. However, for sports and daily activities, action motion's visual tempo and temporal representation learning, similar action categories with long-range temporal dynamic structure, sub-actionsharing within similar action categories, and non-periodic interactions make video action recognition more challenging. Early researchers strived to explore hand-crafted feature-extraction methods for action-recognition tasks, which perform well in simple actions but struggle to handle complex ones, especially for subtle actions characterized by rapid changes over short periods, such as slight hand movements or brief interactions with objects. Researchers have proposed various 2D CNNs [5; 6; 7] and 3D CNNs [8; 9; 10] approaches to learn motion representation for video action recognition, with end-to-end manner. However, motion-tempo modeling at a distinct temporal scale which tends to describe the motion dynamics and the long-term or short-term motion tempos of actions, thus benefits for video action recognition, is often overlooked [11; 12]. For instance, due to the distinctive age, gender, and objects interacted with, the activity habits and motion patterns of persons are different when performing the same action categories. Speed variations result in large intra-class variations. Failing to model the motion tempo and the representation learning of action's dynamic information deteriorates the action-recognition performance, especially for recognizing similar actions with less detailed dynamic or appearance differences, such as \"Long jumping\" and \"Running\", \"Hugging\" and \"Approaching\", \"Pushing\" and \"Kicking\", etc. As shown in Figure 1, ubiquitous sub-action sharing phenomenons between similar action categories make video action recognition more challenging. For example, in the Kinetics-400 dataset [13], the action category \"Triple jump\" is often confused with similar categories like \"High jump\" and \"Long jump\", since they share the common sub-actions \"Approach\" and \"Landing\", which contribute less to distinguishing them. It noted that the unique category-specific sub-action \"Flight\" is crucial and contributes more to the action category \"High jump\". Actually, from Figure 1, it is observed that different action categories have varying motion tempos; \"Take-off\" is a fast-tempo sub-action, while the action-specific sub-actions, such as \"Hop\", \"Skip\", \"Flight\", and \"Action in the air\" have slower tempos. During the training of CNNs, more computational resources are allocated to extract the information of same video frames, but overlook the category-specific motion dynamic tempos, thus struggling to distinguish similar actions. In contrast, the unique sub-action information specific to each category is crucial for recognizing them. For instance, the sub-action \"Action in the air\" is crucial for recognition of the action category \"Long jump\". Therefore,  
Figure 1: From the Kinetics-400 datasets, the eight video frames are sampled from similar action categories “(**a**) Triple jump”, “(**b**) Long jump” and “(**c**) High jump”. It can be observed that they contain the same sub-actions, and have varying motion tempos. effectively capturing and distinguishing the crucial sub-actions of action categories is vital for video action classification.