Human Detection in Video over Large Viewpoint Changes

Human Detection in Video over Large Viewpoint Changes 1249
and gradient of an object to some extent. HOG computes oriented gradient distribution
in a rectangle image window. An edgelet is a short segment of line or
curve, which is predefined based on prior knowledge. 2) Detection over videos
as [1] [2]. Both of them are already mentioned in the previous section. 3) Object
tracking as [8] [9]. Some need manual initializations as in [8], and some are
with the aid of detection as in [9]. 4) Detecting events or human behaviors. 3D
volumetric features [10] are designed for event detection, which can be 3D harr
like features. ST-patch [11] is used for detecting behaviors. Inspired by those
works, we propose Intra-frame and Inter-frame Comparison Features (I 2 CF s)
to combine appearance and motion information.
Due to the large variation in human appearance over wide viewpoint changes,
it is impossible to train a usable detector by taking the sample space as a
whole. The solution is, divide and conquer, to cluster the sample space into
some subspaces during training. A subspace can be dealt with as one class and
the difficulty exists mainly in clustering the sample space. An efficient way is
to cluster sample space automatically like in [12] [13] [14]. Clustered Boosting
Tree (CBT) [14] splits sample space automatically by the already learned discriminative
features during training process for pedestrian detection. Mixture of
Experts [12] (MoE) jointly learns multiple classifiers and data partitions. It emphasizes
much on local experts and is suitable when input data can be naturally
divided into homogeneous subsets, which is even impossible for a fixed viewpoint
of human as shown in Fig. 1. MC-Boost [13] co-clusters images and visual
features by simultaneously learning image clusters and boosting classifiers. Risk
map, defined on pixel level distance between samples, is also used to reduce the
searching space of the weak classifiers in [13]. To solve our problem, we propose
an Enhanced Multiple Clusters Boost (EMC-Boost) algorithm to co-cluster the
sample space and discriminative features automatically which combines the benefits
of Cascade [15], CBT [14] and MC-Boost [13]. The selection of EMC-Boost
instead of MC-Boost is discussed in Sec. 7.
Our contributions are summarized in four folds: 1) Intra-frame and Interframe
Comparison Features (I 2 CF s) are proposed to combine appearance and
motion information for human detection in video over large viewpoint changes;
2) An Enhanced Multiple Clusters Boost (EMC-Boost) algorithm is proposed
to co-cluster the sample space and discriminative features automatically; 3) A
Multiple Video Sampling (MVS) strategy is used to make our approach robust
to human motion and video frame rate changes; 4) A two-stage tree structure
detector is presented to fully mine the discriminative features of the appearance
and motion information. The experiments in challenging real-world scenes show
that our approach is robust to human motion and frame rate changes.
3 Intra-frame and Inter-frame Comparison Features
3.1 Granular space
Our proposed discriminative feature is defined in Granular space [16]. A granule
is a square window patch defined in grey images, which is defined as a triplet