Moving object segmentation and detection for monocular robot based on active contour model

Liu, P.R.; Meng, Max Q.-H.; Liu, P.X.

doi:10.1049/el:20053620

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…2 The method proposed by Lucas and Kanade 3 is one of the most popular versions of optical flow methods for motion detection. Gibson and Spann, 4 Liu et al, 5 Kim and Kak, 6 and ChamorroMartinez and Fernandez-Valdivia 7 have applied optical flow to detect moving objects in video sequences.…”

Section: Introductionmentioning

confidence: 99%

Moving/motionless foreground object detection using fast statistical background updating

Chiu

Tsai

2013

The Imaging Science Journal

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In video surveillance, the detection of foreground objects in an image sequence from a still camera is very important for object tracking, activity recognition and behaviour understanding. The conventional background subtraction cannot respond promptly to dynamic changes in the background, and temporal difference cannot accurately extract the object shapes and detect motionless objects. In this paper, we propose a fast statistical process control scheme for foreground segmentation. The proposed method can promptly calculate the exact grey-level mean and standard deviation of individual pixels in both short-and long-term image sequences by simply deleting the earliest one among the set of images and adding the current image scene in the image sequence. A short-term updating process can be highly responsive to dynamic changes of the environment, and a long-term updating process can well extract the shape of a moving object. The detection results from both the short-and long-term processes are incorporated to detect motionless objects and eliminate non-stationary background objects. Experimental results have shown that the proposed scheme can be well applied to both indoor and outdoor environments. It can effectively extract foreground objects with various moving speeds or without motion at a high process frame rate.

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Section: Introductionmentioning

confidence: 99%

Moving/motionless foreground object detection using fast statistical background updating

Chiu

Tsai

2013

The Imaging Science Journal

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“…The third one uses the monocular vision technique. However, it must have robust features within a specific region [10]. SLAM [11] is an algorithm for localization and mapping simulation and is appreciated by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Stereo vision-based self-localization system for RoboCup

Chiang

Hsia

Hsu

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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This work proposes a new Stereo Vision-Based SelfLocalization System (SVBSLS) for the RoboCup soccer humanoid league rules for the 2010 competition. The humanoid robot integrates the information from the pan/tilt motors and stereo vision to accomplish the self-localization and measure the distance of the robot and the soccer ball. The proposed approach uses the trigonometric function to find the coarse distances from the robot to the landmark and the robot to the soccer ball, and then it further adopts the artificial neural network technique to increase the precision of the distance. The statistics approach is also used to calculate the relationship between the humanoid robot and the position of the landmark for self-localization. The experimental results indicate that the localization system of SVBSLS in this research work has 100% average accuracy ratio for localization. The average error of distance from the humanoid soccer robot to the soccer ball is only 0.64 cm.

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“…The third one uses the monocular vision technique. It must have robust features within a specific region [11]. This work proposes a visual self-localization approach that uses a single CCD camera and pan/tilt motors on the robot head to fmd the robust features and to analyze the environmental information for the RoboCup soccer field of2009 [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient neural network approach of self-localization for humanoid robot

Chang

Hsia

et al. 2009

2009 Joint Conferences on Pervasive Computing (JCPC)

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Robot soccer game is one of the significant and interesting areas among most of the autonomous robotic researches. Following the humanoid soccer robot basic movement and strategy actions, the robot is operated in a dynamic and unpredictable contest environment and must recognize the position of itselfin the fi eld all the time. Therefore, the localization system of the soccer robot becomes the key technology to improve the performance. This work proposes efficient approachesfor humanoid robot and uses one landmark to accomplish the self-localization. This localization mechanism integrates the inf ormation from the pan/tilt motors and a single camera on the robot head together with the artificial neural network technique to adaptively adjust the humanoid robot position. The neural network approach can improve the precision of the localization. The experimental results indicate that the average accuracy ratio is 88.5% underframe rate of 15 frames per second (fps), and the average error for the distance between the actual position and the measured position ofthe object is 6.68cm.

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Moving object segmentation and detection for monocular robot based on active contour model

Cited by 14 publications

References 4 publications

Moving/motionless foreground object detection using fast statistical background updating

Moving/motionless foreground object detection using fast statistical background updating

Stereo vision-based self-localization system for RoboCup

Efficient neural network approach of self-localization for humanoid robot

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