Human
Detection using a Cascade of HOGs
We integrate the cascade-of-rejectors approach with the Histograms of
Oriented Gradients (HoG) features to achieve a fast and accurate human
detection system. The features used in our system are HoGs of variable-size
blocks that capture salient features of humans automatically. Using AdaBoost
for feature selection, we identify the appropriate set of blocks, from a large
set of possible blocks. In our system, we use the integral image representation
and a rejection cascade which significantly speed up the computation. For a 320
× 280 image, the system can process 5 to 30 frames per second depending on the
density in which we scan the image, while maintaining an accuracy level similar
to existing methods.
For each level
of the cascade we construct a strong classifier consisting of several weak
classifiers (linear SVMs in our case). In each level of the cascade we keep
adding weak classifiers until the predefined quality requirements are met. In
our case we require the minimum detection rate to be 0.9975 and the maximum
false positive to be 0.7 in each stage. The training process took a few
days using a PC with 1.8GHz CPU and 2GB memory.
Time required to evaluate a 240 × 320 image. Sparse scan refers to the way used in
Dalal & Triggs experiment. Dense scan is using a smaller step-size to
produce more hypothesis in a testing image. Our method is much faster than the
Dalal & Triggs algorithm, and the gap increases as the scan density
increases. (For the Dalal & Triggs case, the reported results are based on
our implementation of their algorithm). The Rectangular features are the
fastest to compute but its accuracy is not as good.
Results using a dense scan and no post-processing was
applied to the images
• Qiang
Zhu, Shai Avidan, Mei-chen Ye, Kwang-Ting Cheng. Fast human detection using a
cascade of Histograms of Oriented Gradients. IEEE Computer Vision and
Pattern Recognition 2006(CVPR 2006), June 17-22,