1 research outputs found
Learning Sampling Distributions for Efficient Object Detection
Object detection is an important task in computer vision and learning
systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an
algorithm of fast and accurate object detection. By sampling particle windows
from a proposal distribution (PD), MPW avoids exhaustively scanning the image.
Despite its success, it is unknown how to determine the number of stages and
the number of particle windows in each stage. Moreover, it has to generate too
many particle windows in the initialization step and it redraws unnecessary too
many particle windows around object-like regions. In this paper, we attempt to
solve the problems of MPW. An important fact we used is that there is large
probability for a randomly generated particle window not to contain the object
because the object is a sparse event relevant to the huge number of candidate
windows. Therefore, we design the proposal distribution so as to efficiently
reject the huge number of non-object windows. Specifically, we propose the
concepts of rejection, acceptance, and ambiguity windows and regions. This
contrasts to MPW which utilizes only on region of support. The PD of MPW is
acceptance-oriented whereas the PD of our method (called iPW) is
rejection-oriented. Experimental results on human and face detection
demonstrate the efficiency and effectiveness of the iPW algorithm. The source
code is publicly accessible.Comment: 14 pages, 13 figure