Human Detection in Video over Large Viewpoint Changes

1254 G. Duan, H. Ai, and S. Lao
w.r.t. the classifier score. The weight of k th classifier over i th sample is updated
by
w ki = ∂ log J
∂y ki
= t i − P i
P i
P ki (x i ). (11)
We sum up the training algorithms of MC and SC in Table 2.
Table 2: Learning algorithms of MC and SC.
Input: Sample set S = {(x i , y i )|1 ≤ i ≤ m} where y i = ±1; Detection rate r in each layer.
Output: H k (x) = P t α kt(x), k = 1, · · · , K.
Loop: For t = 1, · · · , T
(MC)
P
i w kih kt (x i ).
– Find weak classifiers h t, (h kt = h t, k = 1, · · · , K) that maximize P K
k=1
– Find the weak-learner weights α kt , (k = 1, · · · , K) that maximize Γ (H + α kt h kt ).
– Update weights by Eq.11.
(SC) For k = 1, · · · , K
– Find weak classifiers h kt that maximize P i w kih kt (x i ).
– Find the weak-learner weights α kt that maximize Γ (H + α kt h kt ).
– Update weights by Eq.11.
Update thresholds θ k (k = 1, · · · , K) to satisfy detection rate r.
4.3 A general EMC-Boost
The three components of EMC-Boost have different properties. CC takes all
samples as one cluster while MC or SC considers that the whole sample space
consists of multiple clusters. CC tends to distinguish positives from negatives;
while SC tends to cluster the sample space, and MC can do both work at the
same time but not so accurately as CC in classifying positives from negatives
or as SC in clustering. Compared with SC, one particular advantage of MC is
sharing weak features among all clusters. We combine these three components
to become a general EMC-Boost as shown in Fig. 3 (d) which contains five
steps:(Note that Step 2 is similar to CBT [14])
Step 1. CC learns a classifier for all samples considered as one category.
Step 2. K-means algorithm clusters sample space with learned weak features.
Step 3. MC clusters sample space coarsely.
Step 4. SC clusters the sample space further.
Step 5. CC learns a classifier for each cluster center.
5 Multiple Video Sampling
In order to deal with the change in video frame rate or abrupt motion problem,
we introduce a Multiple Video Sampling (MVS) strategy as illustrated in
Fig. 4 (a) and (b). Considering five consecutive frames in (a), a positive sample
is made up of two frames, where one is the first frame and the other is from the