我该出去走走吗？从视频数据估计能量消耗
Should I take a walk? Estimating Energy Expenditure from Video Data
摘要：
我们探讨的问题，自动推断量的千卡使用人类在体力活动期间，从他/她的视频观察。为了研究这个研究不足的任务，我们引入了Vid 2Burn-一个全源基准，用于从视频数据中估计热量消耗，其中包括高强度和低强度活动，我们根据医学文献中建立的模型推导出能量消耗注释。在实践中，训练集将仅覆盖一定量的活动类型，并且重要的是验证模型是否确实捕获了能量消耗的本质（例如，涉及多少肌肉和哪些肌肉以及它们工作的强度），而不是记忆在训练期间看到的特定活动类别的固定值。理想情况下，模型应该超越这种特定类别的偏见，并回归视频中的卡路里成本，这些视频描述了训练过程中没有明确存在的活动类别。考虑到这一特性，Vid 2Burn附带了一个跨类别的基准，其中的任务是回归训练期间不存在的身体活动类型的热量消耗。一个广泛的评估国家的最先进的方法为视频识别修改的能量消耗估计任务证明了这个问题的难度，特别是在测试时的新的活动类型，标志着一个新的研究方向。数据集和代码可在https://github.com/KPeng9510/Vid2Burn上获得。
英文摘要：
We explore the problem of automatically inferring the amount of kilocalories used by human during physical activity from his/her video observation. To study this underresearched task, we introduce Vid2Burn -- an omni-source benchmark for estimating caloric expenditure from video data featuring both, high- and low-intensity activities for which we derive energy expenditure annotations based on models established in medical literature. In practice, a training set would only cover a certain amount of activity types, and it is important to validate, if the model indeed captures the essence of energy expenditure, (e.g., how many and which muscles are involved and how intense they work) instead of memorizing fixed values of specific activity categories seen during training. Ideally, the models should look beyond such category-specific biases and regress the caloric cost in videos depicting activity categories not explicitly present during training. With this property in mind, Vid2Burn is accompanied with a cross-category benchmark, where the task is to regress caloric expenditure for types of physical activities not present during training. An extensive evaluation of state-of-the-art approaches for video recognition modified for the energy expenditure estimation task demonstrates the difficulty of this problem, especially for new activity types at test-time, marking a new research direction. Dataset and code are available at https://github.com/KPeng9510/Vid2Burn.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
