Human Detection in Video over Large Viewpoint Changes

1252 G. Duan, H. Ai, and S. Lao
memory requirements impractical. With the distance of two granules defined in
Sec. 3.1, two effective constraints are introduced into I 2 CF : 1)Motivated by [5],
the first pair of granules in I 2 CF is constrained as d(g i 1, g j 1 ) ≤ T 1. 2) Considering
of the consistency in one frame or two near video frames, we constrain that the
second pair of granules in I 2 CF is in the neighborhood of the first pair as shown
in Fig. 2 (d):
d(g i 1, g i 2) ≤ T 2 , d(g j 1 , gj 2 ) ≤ T 2. (7)
We set T 1 = 8, T 2 = 4 in our experiments.
Table 1: Learning algorithm of I 2 CF .
Input: Sample set S = {(x i , y i )|1 ≤ i ≤ m} where y i = ±1.
Initialize: Cell space (CS) with all possible cells and empty I 2 CF .
Output: The learned I 2 CF .
Loop:
– Learn the first pair of granules as [5]. Denote the best f pairs as a set F .
– Construct a new set CS’: In each cell of CS’, the first pair of granules is from F , the
second pair of granules is generated by Eq. 7 and its mode is A-mode, D-mode or C-mode.
Calculate Z value of I 2 CF by adding each cell in CS’.
– Select the cell with the lowest Z value, denoted as c ∗ . Add c ∗ to I 2 CF .
– Refine I 2 CF by replacing one or two granules in it without changing the mode.
Heuristically learning I 2 CF starts with an empty I 2 CF . Each time select
the most discriminative cell and add it to I 2 CF . The discriminability of a weak
feature is measured by Z value, which reflects the classification power of the
weak classifier as [17]:
Z = 2 ∑ √
W+W j −, j (8)
j
where W j + is the weight of positive samples that fall into the j th bin while W j −
is that of negatives. The less Z value is, the more discriminative a weak feature
is. The learning algorithm of I 2 CF is summarized in Table 1. (See more details
in [5] [16].)
4 EMC-Boost
We propose the EMC-Boost to co-cluster the sample space and discriminative
features automatically. A perceptual clustering problem is shown in Fig. 3 (a)-
(c). EMC-Boost consists of three components, Cascade Component (CC), Mixed
Component (MC) and Separated Component (SC). The three components are
combined to become EMC-Boost. In fact, SC is similar to MC-Boost [13], which
is the reason that our boosting algorithm is named as EMC-Boost. In the following
descriptions, we formulate the three components explicitly first, and then
demonstrate the learning algorithms, and summarize EMC-Boost at the end of
this section.