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Summary: FAST HUMAN DETECTION USING NODE-COMBINED PART DETECTOR
Song CAO
Department of Electronic Engineering,
Tsinghua University, Beijing 100084, China
Genquan DUAN, Haizhou AI
Department of Computer Science and Technology,
Tsinghua University, Beijing 100084, China
ABSTRACT
Detecting people in occlusion and articulated pose remains a
big challenging problem in computer vision. To achieve a fast
and accurate human detection algorithm, Node-Combined
Part Detector (NCPD) Model is proposed in this paper.
We make two major contributions: (1) We propose a novel
method, torso-nodes combination, to integrate part detectors.
(2) We adopt stable part detectors described by Associated
Paring Comparison Features (APCF) and trained with Real-
AdaBoost algorithm. This new human detection algorithm is
not only much faster than the previous work but also main-
taining competitive accuracy with the state-of-the-art human
detection system. Besides, the algorithm performs better
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