Gaitpoint: A Gait Recognition Network Based on Point Cloud Analysis
Gaitpoint: A Gait Recognition Network Based on Point Cloud Analysis
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DOI：
10.1109/icip46576.2022.9897558
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Jiajing Chen;Huantao Ren;F. Chen;Senem Velipasalar;V. Phoha
中科院分区：
文献类型：
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作者：
Jiajing Chen;Huantao Ren;F. Chen;Senem Velipasalar;V. Phoha
我们提出了一种新的步态识别方法，结合卷积特征和特征的人体姿势的关键点，通过点云分析模型。目前，大多数最先进的步态识别工作仅依赖于图像，并且纯粹基于卷积神经网络。这些方法中的大多数对行走的人的外观的微小变化非常敏感。例如，如果一个人穿着外套或背着包，这些方法的准确性可能会显着下降。为了解决这个问题，我们提出了一个人的关键点序列作为一个点云和联合收割机人体关键点的功能和卷积特征地图的最终预测。实验结果表明，这种方法的承诺，它优于三个国家的最先进的基线在所有的步行场景，包括那些涉及沉重的衣服或携带的物品。
We propose a novel gait recognition method that combines convolutional features with features of human pose key points obtained by a point cloud analysis model. Currently, most state-of-the-art works on gait recognition rely on only images and are purely based on convolutional neural networks. Most of these methods are very sensitive to small variations in the appearance of a walking person. For instance, if a person wears a coat or carries a bag, the accuracy of these methods may drop significantly. To address this problem, we propose to treat a sequence of human key points as a point cloud and combine human key point features and convolution feature map for final prediction. The experimental results show the promise of this approach, which outperforms three state-oft-he-art baselines in all walking scenarios, including the ones involving heavy clothing or carried items.