  Popular Hough Transform-based object detection approaches usually construct
an appearance codebook by clustering local image features. However, how to
choose appropriate values for the parameters used in the clustering step
remains an open problem. Moreover, some popular histogram features extracted
from overlapping image blocks may cause a high degree of redundancy and
multicollinearity. In this paper, we propose a novel Hough Transform-based
object detection approach. First, to address the above issues, we exploit a
Bridge Partial Least Squares (BPLS) technique to establish context-encoded
Hough Regression Models (HRMs), which are linear regression models that cast
probabilistic Hough votes to predict object locations. BPLS is an efficient
variant of Partial Least Squares (PLS). PLS-based regression techniques
(including BPLS) can reduce the redundancy and eliminate the multicollinearity
of a feature set. And the appropriate value of the only parameter used in PLS
(i.e., the number of latent components) can be determined by using a
cross-validation procedure. Second, to efficiently handle object scale changes,
we propose a novel multi-scale voting scheme. In this scheme, multiple Hough
images corresponding to multiple object scales can be obtained simultaneously.
Third, an object in a test image may correspond to multiple true and false
positive hypotheses at different scales. Based on the proposed multi-scale
voting scheme, a principled strategy is proposed to fuse hypotheses to reduce
false positives by evaluating normalized pointwise mutual information between
hypotheses. In the experiments, we also compare the proposed HRM approach with
its several variants to evaluate the influences of its components on its
performance. Experimental results show that the proposed HRM approach has
achieved desirable performances on popular benchmark datasets.
