Opponent zigzag movement model capture and prediction in robotic soccer

Andriana, Dian; Machbub, Carmadi; Prihatmanto, Ary Setijadi

doi:10.1109/idm.2015.7516332

Cited by 1 publication

(1 citation statement)

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“…Previous work [20][21][22][23] has been used successfully transformation function to model the face objects movements. This paper continues the work in combination of the face and body posture movements by utilization of Opencv Dlib face landmarks [24,25], Kinect [5] and image contouring in Opencv, and it is compared with SURF methods [11].…”

Section: Introductionmentioning

confidence: 99%

Combination of Face and Posture Features for Tracking of Moving Human Visual Characteristics

Andriana¹,

Prihatmanto²,

Hidayat³

et al. 2017

ijeei

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Understanding images by recognizing its objects is still a challenging task. Tracking of moving human and recognition have been developed by researchers but not yet shows enough information needed for recognition. Initially, a tracking process of an object starts with detection and recognition of the object in a static pose and position, and then continues in movement in different poses. Available moving human recognition methods still has error in classification and need a huge amount of examples which may still be incomplete. Human face and body posture characteristics such as size of the eyes, nose, mouth, or fat or thin bodies, are important visual features in different poses for personal identification to increase accuracy of human recognition system, and it is still rare in researches. This paper attempts to describe visual features that best known for human, but hard to be recognized by machines. Curve fitting approaches to face and body posture features are also introduced to capture exact patterns of the features. Body postures are also preprocessed with a Kinect depth camera, and also compared to popular and recent methods of visual object recognition. Finally, we demonstrate our method can be useful for visual object classification. Probabilities of personal identification can be increased by using different poses and characteristics of smaller detail features through body postures and face areas. More detail features will richen comparison data samples for higher recognition accuracy.

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