摘要：

人体异常行为研究是应对人体潜在危险和紧急情况的重要保障任务。针对人体异常行为定义模糊、缺乏标准数据集等问题，基于生活场景定义头痛、摔倒、抽搐、腰痛、拳打、踢踹6种高发生频率的人体异常行为，并自建数据集HABDataset-6。基于注意力机制的TimeSformer算法在自建数据集HABDataset-6上存在高损失和时间序列建模不全面的问题，难以提取复杂样本的特征。为了更好地处理人体异常行为数据，提出改进算法TS-AT。首先采用加速随机梯度下降(ASGD)优化算法改进交叉熵损失函数来设计CAS模块降低原算法损失值，其次嵌入时间偏移模块(TSM)到原算法的Backbone网络中，提高时间序列的感知能力，提取更优特征用于模型训练。实验结果表明：TS-AT算法在自建数据集HABDataset-6上取得了良好效果，各行为类别的平均推理准确率高于80%；在公开数据集UCF-10和老人异常行为数据上，平均测试准确率分别达到了99%和84%，超过了对比算法。这些结果表明TS-AT算法在人体异常行为识别方面具有更高的精确度和良好的鲁棒性，有望提高应对潜在危险和紧急情况的能力，进一步保障人们的安全与健康。

关键词: 人体异常行为, TimeSformer算法, 时间序列, 优化算法, 时间偏移模块

Abstract:

Research on abnormal human behavior is an important safeguarding task to deal with potential dangers and emergencies. In view of the fuzzy definition of abnormal human behavior and the lack of standard datasets, this study defines six high-frequency abnormal human behaviors based on life scenarios—namely Headache, Fall, Twitch, Lumbago, Punch, and Kick—and independently constructs a dataset known as HABDataset-6. The attention mechanism in TimeSformer can be used to process this self-built dataset; however, it suffers from high loss and incomplete time series modeling, making it difficult to extract features from complex samples. Therefore, this study uses the Accelerating Stochastic Gradient Descent (ASGD) optimization algorithm to improve the cross-entropy loss, that is, a CAS module is proposed that reduces the loss value of the original algorithm. Second, a Temporal Shift Module (TSM) is embedded in the backbone network of the original algorithm to improve the perception ability of the time series to extract better features for model training. Then, the study integrates CAS and TSM and proposes the TS-AT algorithm, achieving good results on the self-built dataset with a reasoning accuracy of more than 80% for each behavior category. The usability of the TS-AT algorithm is tested on the public dataset, UCF-10, and on the elderly abnormal behavior data, and it achieves average test accuracies of 99% and 84%, respectively, exceeding those of advanced algorithms. These results show that the TS-AT algorithm has higher accuracy and good robustness in identifying abnormal human behavior and is expected to improve the ability to respond to potential dangers and emergencies and further ensure people's safety and health.

Key words: abnormal human behavior, TimeSformer algorithm, time series, optimization algorithm, Temporal Shift Module (TSM)