Abstract
Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from the deep learning community in recent years. Most of the existing deep learning works were designed based on pre-segmented sensor streams and they have treated activity segmentation and recognition as two separate tasks. In practice, performing data stream segmentation is very challenging. We believe that both activity segmentation and recognition may convey unique information which can complement each other to improve the performance of the two tasks. In this paper, we firstly proposes a new multitask deep neural network to solve the two tasks simultaneously. The proposed neural network adopts selective convolution and features multiscale windows to segment activities of long or short time durations. First, multiple windows of different scales are generated to center on each unit of the feature sequence. Then, the model is trained to predict, for each window, the activity class and the offset to the true activity boundaries. Finally, overlapping windows are filtered out by non-maximum suppression, and adjacent windows of the same activity are concatenated to complete the segmentation task. Extensive experiments were conducted on eight popular benchmarking datasets, and the results show that our proposed method outperforms the state-of-the-art methods both for activity recognition and segmentation.
Abstract (translated)
基于传感器的人动活动分割和识别是许多实际应用场景中两个重要且具有挑战性的的问题,近年来已经吸引了深度学习社区越来越多的关注。现存的深度学习工作大多数是基于已segmented传感器流设计的,并将其活动分割和识别视为两个独立的任务。在实践中,进行数据流分割非常困难。我们相信,活动分割和识别可能传递独特的信息,可以互相补充,以改善两个任务的表现。在本文中,我们首先提出了一种新的多任务深度神经网络,以同时解决这两个任务。该神经网络采用选择性卷积和特征多尺度窗口来分割长或短持续时间的活动。首先,多个不同尺度的窗口将被生成,以集中在每个特征单元上。然后,模型被训练以预测,对每个窗口,活动类别和真正的活动边界offset。最后,重叠窗口通过非最大值抑制被过滤出来,同时相同的活动相邻窗口被拼接起来,以完成分割任务。对八个常用的基准数据集进行了广泛的实验,结果表明,我们提出的方法在活动识别和分割方面都优于现有的方法。
URL
https://arxiv.org/abs/2303.11100
