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首页> 外文会议>IEEE/RSJ International Conference on Intelligent Robots and Systems >Single-shot clothing category recognition in free-configurations with application to autonomous clothes sorting
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Single-shot clothing category recognition in free-configurations with application to autonomous clothes sorting

机译：自由配置中的单次拍摄服装类别识别，并应用于自动服装分类

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This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.
机译：本文提出了一种单次使用的方法，可以从2.5D特征中识别服装类别。我们为此任务提出了两个视觉功能，即BSP（B样条补丁）和TSD（拓扑空间距离）。本地BSP功能由LLC（本地约束线性编码）编码，并与三个不同的全局功能融合。我们的视觉特征对变形形状具有鲁棒性，并且我们的方法能够识别不受约束和随机配置的未知服装类别。我们将类别识别流水线与立体视觉系统，服装实例检测和双臂机械手集成在一起，以实现自主分类系统。为了验证我们提出的方法的性能，我们建立了一个高分辨率的RGBD服装数据集，该数据集包含以随机配置采样的5个类别的50个服装项目（总共2100个服装样本）。实验结果表明，我们的方法在对训练中以前看不到的衣物进行分类时，能够达到83.2％的准确率。这比以前的最新技术提高了36.2％。最后，我们在自动机器人分类系统中评估了该方法，在该系统中，机器人从不受约束的堆中识别出衣物，将其抓起，然后根据类别将其分类到一个盒子中。我们提出的分类系统可实现单发感知的合理分类成功率。

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