Human Activity Recognition Using Body Contour Parameters Extracted from Depth Images

IPSJ Transactions on Computer Vision and Applications

Sekine

et al. 2015

Self Cite

Saliency maps as visual attention computational models can reveal novel regions within a scene (as in the human visual system), which can decrease the amount of data to be processed in task specific computer vision applications. Most of the saliency computation models do not take advantage of prior spatial memory by giving priority to spatial or object based features to obtain bottom-up or top-down saliency maps. In our previous experiments, we demonstrated that spatial memory regardless of object features can aid detection and tracking tasks with a mobile robot by using a 2D global environment memory of the robot and local Kinect data in 2D to compute the space-based saliency map. However, in complex scenes where 2D space-based saliency is not enough (i.e., subject lying on the bed), 3D scene analysis is necessary to extract novelty within the scene by using spatial memory. Therefore, in this work, to improve the detection of novelty in a known environment, we proposed a space-based spatial saliency with 3D local information by improving 2D space base saliency with height as prior information about the specific locations. Moreover, the algorithm can also be integrated with other bottom-up or top-down saliency computational models to improve the detection results. Experimental results demonstrate that high accuracy for novelty detection can be obtained, and computational time can be reduced for existing state of the art detection and tracking models with the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Spatial Visual Attention for Novelty Detection: A Space-based Saliency Model in 3D Using Spatial Memory

IPSJ Transactions on Computer Vision and Applications

Sekine

et al. 2015

Self Cite

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“…At-home biomonitoring systems have become an important solution in the medical field as an assistive technology for the people who have difficulties leaving their houses, such as elderly people or motor-function impaired persons (MIPs) [ 1 – 8 ]. Benefits of these systems include more convenient and comfortable ways of healthcare for patients and reduction in the workload of the therapists [ 5 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, smart houses do not have these disadvantages; daily activities can be tracked by systems that include multiple observation devices such as motion tracking systems, cmos/ccd cameras, and color and infrared cameras such as Kinect [ 8 , 10 , 11 , 17 , 18 ]. For instance, Tamura et al [ 7 ] proposed an at-home biomonitoring system in which automated electrocardiogram (ECG) signals are measured and observed in some of the arranged places at home, such as bed, toilet, or bathroom, without using body surface electrodes.…”

Section: Introductionmentioning

confidence: 99%

“…As proposed in our previous works [ 5 , 8 , 19 , 20 ], our main idea is to use assistive robotics to support elderly or motor-function impaired persons biomonitoring. Although a mobile system brings more complexity to handle daily observation tasks, a mobile robot solution includes several advantages: (i) it is a cheaper smart system than smart houses, (ii) it could make full use of the capability of sensors, since the sensors could be brought to a position and an angle optimal or suboptimal to observation, (iii) it avoids the multiple vision modules by using a mobile monitoring system, and (iv) it prevents the need of the subjects for wearing any sensors [ 5 , 8 , 19 , 20 ] compared to wearable sensor applications. These advantages make the mobile robot a good candidate for assistive technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in our previous research [ 5 , 8 , 19 , 20 ], we presented an at-home biomonitoring mobile robot project to improve the quality of life of motor-function impaired persons (MIPs). The robot observes the subject and recognizes the activity he is performing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

The Scientific World Journal

Wei

et al. 2014

Self Cite

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

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Active Sensing for Human Activity Recognition by a Home Bio-monitoring Robot in a Home Living Environment

Nakahata

Advances in Intelligent Systems and Computing

et al. 2017