Fast Human Detection Using Node-Combined Part Detector

2011 18th IEEE International Conference on Image Processing
FAST HUMAN DETECTION USING NODE-COMBINED PART DETECTOR
Song CAO
Department of Electronic Engineering,
Tsinghua University, Beijing 100084, China
Genquan DUAN, Haizhou AI
Department of Computer Science and Technology,
Tsinghua University, Beijing 100084, China
ABSTRACT
Detecting people in occlusion and articulated pose remains a
big challenging problem in computer vision. To achieve a fast
and accurate human detection algorithm, Node-Combined
Part Detector (NCPD) Model is proposed in this paper.
We make two major contributions: (1) We propose a novel
method, torso-nodes combination, to integrate part detectors.
(2) We adopt stable part detectors described by Associated
Paring Comparison Features (APCF) and trained with Real-
AdaBoost algorithm. This new human detection algorithm is
not only much faster than the previous work but also maintaining
competitive accuracy with the state-of-the-art human
detection system. Besides, the algorithm performs better
within low false alarm. For average time per image, our algorithm
can achieve speedup rate of about 10x as compared
with Deformable Part based Model (DPM) and over 125x as
compared with Poselet Model.
Index Terms— Object Detection, Node-Combined Part
Detector, Occlusion, High Articulation
1. INTRODUCTION
Object detection is to locate objects in images, e.g. face detection
[1] and pedestrian detection [2], which is well studied
in computer vision. However, Detecting people in occlusion
and high articulation remains a big challenge. There are
mainly two difficulties for human detection: 1) Humans are
non-rigid objects which cause variations in contour, shape and
color, thus it is hard to use one holistic classifier to describe
all the situations and variations. 2) There are occlusions, due
to a multitude of occluding accessories such as backpacks,
clothes, bags, or due to other persons and objects. To handle
this challenge, part based model becomes popular [3] [4] [5],
which can be regarded as providing more variables to describe
a highly varied object. But how shall we select and train these
part detectors? How to integrate them into an efficient robust
human detector?
Various algorithms have been proposed for human detection
to deal with occlusion or articulated pose. Deformable
Part based Model (DPM) [5] based on Histograms of Oriented
Gradients (HOG) features [2] combined with Latent Support
Vector Machines (LSVM) training strategy was proposed
in [5] for object detection, in which several part detectors are
learned within the model root (a bound box of object). The
authors established a star model which made each part detector
has its deformable position relationship with the model
root. The inner part detectors contribute to a better description
of inner details of an object, which explores more information
for object detection.
Poselet is an innovative work that was first proposed in
[6], which achieves state-of-the-art results in the detection
and segmentation of human in PASCAL Visual Object Classes
(PASCAL VOC) [7]. In Poselet, the authors randomly select
patches from the training images as seed poselets (poselet
can be folded hands, occluded legs, hands holding up and so
on). Each poselet is described by HOG feature and trained
with linear SVM. Then the random selected poselet detectors
are cluttered and have their own prediction of potential
human location. Many weak and random selected poselets
indicate human position and achieve state-of-the-art results in
PASCAL VOC human detection in the recent several years.
However, two issues exist in the Poselet based detection algorithm.
The first issue is that it is relatively time-consuming
because much of the time is spent on the detection of poselets
and exploiting context among poselets. The other one is
that most of the random selected poselet detectors have a relative
low accuracy and most of poselets indicate the same body
parts like face and head shoulder.
Reviewing progress of detection problems, Boosting
trained detector, eg. face detection [1], pedestrian detection
[8] has proven to be efficient and accurate. To achieve a highly
efficient detection algorithm, we propose Node Combined
Part Detector (NCPD) Model which involves four stable part
detectors described by Associated Paring Comparison Features
(APCF) and trained with Real-AdaBoost algorithm.
Our approach is an experimental study on AdaBoost based
part detectors for human detection.
We consider precise and well-trained part detectors are
the key to real-time human detection in occlusion and high
articulation. We pick up several stable part detectors integrated
by the torso-nodes as demonstrated in Fig.1. We consider
our stable part detectors should not only have a high detection
accuracy, but also cover most of poselets used in [4].
Therefore, in implementation, four stable part detectors (i.e.
face, head shoulder, upper body, whole body) are adopted.
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2011 18th IEEE International Conference on Image Processing
We integrated stable part detectors through torso-nodes to establish
our NCPD Model. This new human detection algorithm
can speed up the detection procedure significantly while
maintaining an competitive accuracy similar to the existing
state-of-the-art methods.
Fig. 1. NCPD Model. The left image is the structure of our
NCPD model. The right image explicitly demonstrates our
stable part detectors
Our contributions are summarized as follows: (1) Node-
Combined Part Detector (NCPD) Model is proposed to integrate
stable part detectors with torso-nodes. (2) Stable
part detectors are learned by AdaBoost using APCF features
which obtains high efficiency in human detection.
The rest of this paper is organized as follows: The following
Section gives the overview of our approach. Section
3 presents the NCPD Model proposed in this paper. While
in Section 4, we demonstrate the training methods of our stable
part detectors, Quantitative experiments and evaluations
on PASCAL VOC test datasets are carried out in Section
5. Finally, conclusion and future work are offered in the last
Section.
2. OVERVIEW OF OUR APPROACH
Our approach mainly contains three steps. The first step is to
train our part detectors. To improve the human detection accuracy,
we should require our part detectors to be robust with
fewer variations. Based on such an idea, we train detectors for
parts, e.g. face, head shoulder, upper body and whole body
which will be explicitly explained in Sec.4. The second step
is to integrate our stable part detectors as an efficient robust
human detector. We propose Node-Combined Part Detector
(NCPD) Model in Sec.3.2, where each stable part detector
has a prediction of the position of torso-nodes. Finally, postprocessing
is made by non-maximum suppression. Following
this procedure, we obtain our efficient human detector which
achieves competitive results in several challenging datasets.
3. NODE COMBINED PART DETECTOR (NCPD)
MODEL
3.1. Stable Part Detectors
We consider that a human in high articulation and occlusion
can be described by many variables. Assuming there are N
poselets in the human detection system where each poselet
represents a variable, thus each person can be described by
a N-length vector based on poselets representation. However,
in a detection problem, we should acknowledge that a N
(usually N > 150) dimension space is large and extensively
makes detection task more complexity. By observing that
some variables are redundant and represent the same semantic
meanings (e.g. many poselets are similar to face), we consider
further reducing the dimension space by using limited, but
principal variables. In practical, we suggest to use stable part
detectors as the principle variables which have fewer variations
in a highly articulated or occluded human. Motivated
by [3], we define our part detectors to be face, head shoulder,
upper body and whole body. These four detectors are stable
and are suitable for human detection. Even in Poselet framework,
most of the effective poselets are similar to these four
body parts, and on the other hand, these four stable parts nearly
cover most of useful poselets when poselets are applied in
detection task. We have also considered adding in more stable
detectors like legs, left body and right body in our algorithm.
However, these detectors are in large variations and less discriminative
as compared with background. To achieve high
accuracy and efficiency, we do not adopt them in our current
algorithm.
3.2. Integration of Stable Part Detectors
Reviewing other tree structure models [5] [9], all the parts
are integrated by one model root. Observing some empirical
knowledge that torso is always under the head with fewer s-
patial variations, similar to Pictorial Structure [9] [10], our
Node-Combined Part Detector (NCPD) Model is established
in which torso is set as its root. However, different from [10],
we adopt a new method, named as torso-nodes combination,
to integrate our stable part detectors into an efficient robust
human detector. Our method, applying Hough voting idea,
uses the distribution of root configuration instead of root s-
patial center, to integrate our stable part detectors. After detection
procedure of all four stable part detectors, assuming
we get n part recalls where we rank them descending with
detection scores as P 1 , P 2 , . . . , P n . Specifically, P 1 is the
highest-probability part recalls. Let L i (N 1 i , N2 i , N3 i , N4 i ) represent
the root configuration of each part P i . We can particularly
consider L i as the torso-nodes distribution, where N k i
is a Gaussian Distribution trained from training dataset. (In
implementation, four torso-nodes refer to left/right shoulders
and left/right hips). We integrated two part detector recalls i
and j using Kullback-Leibler divergence as follows:
4∑
S ij = D KL (N k i , N k j ) + D KL (N k i , N k j ) (1)
k=1
where S ij is an integration distance. If S ij is no larger
than a threshold, then part P i and part P j belong to the same
person. We consider integrating part recalls from the highest
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2011 18th IEEE International Conference on Image Processing
score one. We adopt this greedy search procedure because it
utilizes the most reliable information first which owns a computational
advantage. We sum up all the part recalls which
belong to one potential human location as the final human detection
score. Therefore, we integrate our stable part detectors
under the framework of spatial consistence with the information
from less varied torso-nodes. An example of integration
strategy is demonstrated in Fig.2.
Fig. 2. Integration of Stable Part Detectors. Red, yellow and
blue bound boxes demonstrate detection recalls of face, head
shoulder and upper body respectively. As the torso-nodes distribution
of face and upper body are close, they are integrated
into the same potential human location.
4. TRAINING STABLE PART DETECTORS
4.1. Weak Features
Previously, HOG feature combined with linear SVM is a classic
method in pedestrian detection which has the advantage
of capturing gradient information except its high computation
complexity in both memory and time. We consider that both
gradient and appearance features are important in a detection
procedure, therefore we adopt Associated Paring Comparison
Features (APCF) [8] which has been proved very efficient
and accurate in pedestrian detection. APCF is a feature
which describes invariance of color and gradient of an object
to some extent and it contains two essential elements, Pairing
Comparison of Color (PCC) and Pairing Comparison of Gradient
(PCG). A PCC is a Boolean color comparison of two
granules and a PCG is a Boolean gradient comparison of two
granules in which a granule is a square window patch. For
more details, please refer to [8].
4.2. The Training Algorithm
The Real AdaBoost [11] is used to learn Nested Cascade Detector
[12] for part detection. For interested readers, please
refer to [11] [12] for more details.
5. EXPERIMENTS
We use the PASCAL VOC 2009 training dataset for training,
where we annotated the position of the four stable parts and
torso-nodes. To demonstrate the effectiveness and efficiency
of our NCPD Model, we make the experiments on PAS-
CAL VOC test dataset, using the same criteria as the PAS-
CAL VOC detection competition, that is, the detection can be
regarded as true positive only if it gets a ratio of overlap area
to union area up to 50%. However, not as previous work in
Deformable Part based Model (DPM) and Poselet, we do not
use a bound box adjustment strategy as post-processing procedure,
though according to reports, this adjustment strategy
will improve the detection average precision for about 1% to
3%. All experiments are tested on a computer with Intel Core
2, 2.63GHz, 4GB RAM.
Performance comparison. We compare the detection accuracy
with two of the best human detection methods, Deformable
Part based Model (DPM) and Poselet. The comparison
with our NCPD Model is shown in Fig.3. These ROC
curves are based on the part of PASCAL test dataset which
were released with annotations. It can be found that our model
(NCPD Model) gives relatively higher detection rate by 5% to
some extent as compared with existing methods. We achieve
better detection accuracy than Poselet in PASCAL VOC 2008
and 2010, while in PASCAL VOC 2009, we obtain a similar
performance. However, we do not outperform Deformable
Part based Model (DPM) in PASCAL VOC 2010.
Speed comparison. We test our model for the speedup
rate. The average times per image for each model and NCPD
model speedup rate are summarized in Table 1 and Table 2
where PASCAL VOC 2008, 2009 and 2010 test dataset were
used. It shows that Poselet is a time-consuming method. Our
NCPD Model is faster than DPM, and achieves speedup rate
for about 10x, and 125x as compared with Poselet. We admit
that cascade DPM [13] has already improved the speed of
DPM. However, our method still reach a speedup rate about
2x. While as reported in [13], to achieve high efficiency,
cascade DPM might suffer a loss in accuracy comparing with
original version of DPM.
Fig.4 shows some results comparing Poselet with our
method. Our method can better deal with occlusion and articulated
pose (e.g. (a)(b) in Fig.4) than Poselet. Also, our
NCPD model shows its effectiveness when integrating part
detectors (e.g. (c)(d) in Fig.4). This torso-nodes combination
idea helps us get higher performance in low false alarm rate
by effectively integrating our boosted stable part detectors.
Therefore, we achieve a fast and accurate human detection
algorithm using our NCPD model.
Table 1. Average time per image for different models.
average time per image
PASCAL test dataset 2008 2009 2010
Poselet 112s 118s 121s
DPM 8.95s 9.03s 9.01s
NCPD model 0.89s 0.87s 0.93s
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2011 18th IEEE International Conference on Image Processing
Fig. 3. ROC curves comparison for three different models. (a) PASCAL 2008 dataset (197 pictures, 412 annotations). (b)
PASCAL 2009 dataset (72 pictures, 162 annotations). (c) PASCAL 2010 dataset (505 pictures, 737 annotations)
Table 2. NCPD model speedup rate.
average time per image
PASCAL test dataset 2008 2009 2010
cf. Poselet 125.8x 135.6x 130.1x
cf. DPM 10.1x 10.4x 9.7x
Fig. 4. Detection Results. The first row is the detection results
of Poselet. The second row is the detection results of our
NCPD model
6. CONCLUSION
In this paper, we focus on human detection in occlusion and
high articulation which remains a challenging problem in
computer vision. We propose Node-Combined Part Detector
(NCPD) Model which integrates stable part detectors using
less varied torso-nodes into an efficient and robust human
detector. Different from most previous part based work, we
use AdaBoost with APCF features to train our part detectors.
Our approach is well performing in occlusion and high articulation,
and it demonstrates competitive detection accuracy
and fast speed for human detection. We conclude that the
model described in this paper for detecting people is equally
applicable to other object categories. This is the subject of an
ongoing research.
7. ACKNOWLEDGEMENT
This work is supported by National Science Foundation of
China under grant No.61075026.
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