A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navi...A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.展开更多
Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data i...Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data is still a big challenge. Recently, with the emergence of inexpensive RGB-D devices, this challenge can be better addressed by leveraging additional depth information. A very special yet important case of object recognition is hand-held object recognition, as manipulating objects with hands is common and intuitive in human-human and human-machine interactions. In this paper, we study this problem and introduce an effective framework to address it. This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information. In the object recognition stage, this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy. In particular, we incorporate handcrafted and deep learned features and study several multi-step fusion variants. Experimental evaluations validate the effectiveness of the proposed method.展开更多
提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的...提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的模型对目标进行定位。实验结果表明:该方法在摄像机运动-目标运动情况下,能对局部特征未知或特征不明显的动态目标进行有效的识别与定位。展开更多
基金Supported by the National Natural Science Foundation of China(61103157)Beijing Municipal Education Commission Project(SQKM201311417010)
文摘A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.
基金This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2012CB316400, the National Natural Science Foundation of China under Grant Nos. 61322212 and 61450110446, the National High Technology Research and Development 863 Program of China under Grant No. 2014AA015202, and the Chinese Academy of Sciences Fellowships for Young International Scientists under Grant No. 2011Y1GB05. This work is also funded by Lenovo Outstanding Young Scientists Program （LOYS）.
文摘Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data is still a big challenge. Recently, with the emergence of inexpensive RGB-D devices, this challenge can be better addressed by leveraging additional depth information. A very special yet important case of object recognition is hand-held object recognition, as manipulating objects with hands is common and intuitive in human-human and human-machine interactions. In this paper, we study this problem and introduce an effective framework to address it. This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information. In the object recognition stage, this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy. In particular, we incorporate handcrafted and deep learned features and study several multi-step fusion variants. Experimental evaluations validate the effectiveness of the proposed method.
文摘提出了一种双目移动机器人实时动态目标识别与定位方法。该算法首先采用SIFT(Scale Invariant Features Transforms)算法提取目标特征,并结合双目视差特征进行目标匹配;然后通过区域增长方法进行目标区域的提取;最后结合双目视觉标定的模型对目标进行定位。实验结果表明:该方法在摄像机运动-目标运动情况下,能对局部特征未知或特征不明显的动态目标进行有效的识别与定位。
