A Real-Time Human Detection System for Video

Bobo ZENG  Guijin WANG  Xinggang LIN  Chunxiao LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.7   pp.1979-1988
Publication Date: 2012/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1979
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
human detection,  human tracking,  real-time detection,  normalized oriented gradient channel feature,  

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This work presents a real-time human detection system for VGA (Video Graphics Array, 640480) video, which well suits visual surveillance applications. To achieve high running speed and accuracy, firstly we design multiple fast scalar feature types on the gradient channels, and experimentally identify that NOGCF (Normalized Oriented Gradient Channel Feature) has better performance with Gentle AdaBoost in cascaded classifiers. A confidence measure for cascaded classifiers is developed and utilized in the subsequent tracking stage. Secondly, we propose to use speedup techniques including a detector pyramid for multi-scale detection and channel compression for integral channel calculation respectively. Thirdly, by integrating the detector's discrete detected humans and continuous detection confidence map, we employ a two-layer tracking by detection algorithm for further speedup and accuracy improvement. Compared with other methods, experiments show the system is significantly faster with 20 fps running speed in VGA video and has better accuracy as well.