Human Detection Algorithm Based on Edge Symmetry

Wang, Hao; Chen, Jinjin; Fang, Baofu; Dai, Shuanglu

doi:10.1007/978-3-319-16841-8_65

Cited by 3 publications

(1 citation statement)

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“…Among those existing recognition techniques for human motions, head detection and tracking is basis in all human motion recognitions. Many efforts have been devoted to detect and trace the human head using a variety of cue information such as edge [4], texture [5] and shape [6] in two-dimensional (2D) image during the past decades. However, it is still very challenging in case of large pose variations and light changes.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Head Tracking Using Adaptive Local Binary Pattern in Depth Images

Kim¹,

Yoon

2016

IJFIS

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Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

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Section: Introductionmentioning

confidence: 99%