Spatial Relation Model for Object Recognition in Human-Robot Interaction

Cao, Lu; Kobayashi, Yoshinori; Kuno, Yoshinori

doi:10.1007/978-3-642-04070-2_63

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Linguistic studies show the importance of spatial representation in language [1]. Our analysis also shows that people tend to use spatial concepts in performing navigation tasks, particularly, when they encounter objects that are similar or unfamiliar in category [2]. Spatial expressions partition the space into loose regions such as near, back and left.…”

Section: Introductionmentioning

confidence: 73%

Spatial resolution for robot to detect objects

Cao

Kobayashi

Kuno

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In this paper, we report on our development of a robotic system that assists people in accomplishing simple tasks in daily life (e.g., retrieving objects for handicapped and elderly people). These tasks, inevitably involve detecting various kinds of objects. In particular, here, we present an interactive method to detect objects using spatial information. Our experimental results confirm the usefulness and efficiency of our system. We also show how the approach can be improved and highlight necessary directions for future research.

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Section: Introductionmentioning

confidence: 73%

Spatial resolution for robot to detect objects

Cao

Kobayashi

Kuno

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Representing spatial relationships by a set of numerical features is a challenging problem. We should note that the definition of the spatial reference is an important step for any attempt to model spatial relationships (Colliot et al , 2006;Cao , 2009). In our case, due to the nature of the viewpoint, and because the objects of interest are centered in the image, the spatial reference is naturally selected as the central point in the image.…”

Section: • Grouping Of Edge Points E I Into Edge-chainsmentioning

confidence: 99%

“…However, the handling of such spatial relationships is not obvious since symbolic/linguistic reasoning is more or less involved (Erus and Lomenie , 2005) and the concept of spatial ontology is not straightforwardly usable in current image processing lines (Hudelot , 2005). However preliminary examples of the use of spatial relations in a recognition task can be found for example in Colliot et al (2006);Cao (2009).…”

Section: Introductionmentioning

confidence: 99%

How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Erus

Loménie

2010

Pattern Recognition Letters

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To cite this version:Guray Erus, Nicolas Lomenie.How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?.Elsevier AbstractWith the new generation of satellite systems, very high resolution satellite images will be available daily at a high delivery rate. The exploitation of such a huge amount of data will be made possible by the design of high performance analysis algorithms for decision making systems. In particular, the detection and recognition of complex man-made objects is a new challenge coming with this new level of resolution. In this study, we develop a system that recognizes such structured and compact objects like bridges or roundabouts. The original contribution of this work is the use of structural shape attributes in an appearance based statistical learning method framework leading to valuable recognition and false alarm rates. This hybrid structural/statistical approach aims to construct an intermediate step between the low-level image characteristics and high-level semantic concepts.

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“…Traditional object detection algorithms usually scan the entire image using a multiscale sliding window; find the Region of Interest (ROI) [12] that may exist in the image; use the Scale-Invariant Feature Transformation (SIFT) [13], the Histogram of Oriented Gradient (HOG) [14], the Deformable Part-based Model (DPM) [15], the Local Binary Pattern (LBP) [16], and other algorithms to extract the expected features of the ROI; and, finally, use Support Vector Machine (SVM) [17][18][19], iterative adaptive boosting algorithm (Adaboost) [20], or other classifiers to classify and recognize the object. Expected features are generally defined artificially, such as edge, color, shape, and size [21][22][23][24]. The traditional object detection algorithms have pretty good accuracy in fixed scenes, but the accuracy depends on artificially designed features.…”

Section: Introductionmentioning

confidence: 99%

Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots

Ye,

Ma,

Zhou

et al. 2023

Sensors

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Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision.

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Spatial Relation Model for Object Recognition in Human-Robot Interaction

Cited by 6 publications

References 10 publications

Spatial resolution for robot to detect objects

Spatial resolution for robot to detect objects

How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots

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