Pedestrian Detection Based on Deep Convolutional Neural Network with
Ensemble Inference Network
Hiroshi Fukui 1 Takayoshi Yamashita 1 Yuji Yamauchi 1 Hironobu Fujiyoshi 1 Hiroshi Murase2
Abstract— Pedestrian detection is an active research topic fordriving assistance systems. To install pedestrian detection in a
regular vehicle, however, there is a need to reduce its cost andensure high accuracy. Although many approaches have beendeveloped, vision-based methods of pedestrian detection are
best suited to these requirements. In this paper, we propose themethods based on Convolutional Neural Networks (CNN) that
achieves high accuracy in various fields. To achieve such generalization, our CNN-based method introduces Random Dropout
and Ensemble Inference Network (EIN) to the training andclassification processes, respectively. Random Dropout selects
units that have a flexible rate, instead of the fixed rate inconventional Dropout. EIN constructs multiple networks thathave different structures in fully connected layers. The proposed
methods achieves comparable performance to state-of-the-art
methods, even though the structure of the proposed methods
are considerably simpler.

I. INTRODUCTIONIn driving assistance systems, object detection is generally
conducted using Light Detection and Ranging (LIDAR) or
vision-based approaches that use single or multiple frames
recorded by an in-vehicle camera. LIDAR captures a threedimensional point cloud from the light reflected by an object.
Google automatic driving system employs LIDAR because
it records high-quality depth information and is adequate
in the research field. However, LIDAR is a very expensive
technology for regular vehicles.
There are two approaches using in-vehicle cameras: methods based on a single frame and those based on multiple
frames. Although single-frame methods enable real-time
detection, it is required that improve the detection accuracy
in various situations. On the other hand, multiple-frame
techniques achieve high detection accuracy via the use of
motion and context information. The motion information
is extracted from the optical flow, and context information
is estimated by superpixels. These techniques extract richinformation, but are time consuming. In addition, the motion
information requires corresponding points between frames tobe detected. Single-frame methods can be combined withother approaches. For example, LIDAR-based approaches
use single-frame pre-processing to reduce the search region.
In this paper, we consider a single-frame method with theaim of popularizing driving assistance systems based on invehicle cameras.
The combination of Histograms of Oriented Gradients(HOG) features and Support Vector Machines (SVM) has
1 ChubuUniversity, 1200 Matsumoto cho, Kasugai, Aichi, Japan.

fhiro@vision.cs.chubu.ac.jp

2 Nagoya University, Furo cho, Chikusa ku, Nagoya, Aichi, Japan.

commonly been used for pedestrian detection [1]. HOG
features focuses on the gradient of the local region, and isrobust to small variations in pose. Following Dalal work,
several related approaches have been proposed [2] [3] [4][5]. To handle large pose variations, Felzenszwalb proposed
the Deformable Part Model (DPM) [2], which detects pedestrians with a whole pedestrian model and part regions.
DPM attained the best performance in a pedestrian detection
benchmark.
Pedestrian detection based on Deep Convolutional Neural
Networks (CNN) achieved the high detection accuracy inthe pedestrian detection benchmark [6]. The CNN proposedby LeCun has been attracting attention [7], and Krizhevsky
applied CNN to object recognition [11]. The CNN employs a
Rectified Linear Unit (ReLU) as an activation function, anduses Dropout to obtain generalization [8]. Dropout randomly
removes a fixed ratio of units. This ratio is usually set to 50%,
and the response value of selected units is zero. Different
units are selected in each iteration of the training process.
Although networks trained with Dropout exhibit improved
generalization performance, this random selection is only
applied to the training process.
In this paper, to achieve better generalization, we propose
Random Dropout and Ensemble Inference Network (EIN)for the training and classification processes, respectively.
Random Dropout selects units at random with a flexible rate,instead of the fixed rate used in conventional Dropout. EIN
constructs multiple networks that have different structures infully connected layers. In the remainder of this paper, we firstdescribe some related work on conventional CNN. We thenintroduce the proposed Random Dropout and EIN method.
Finally, we investigate the architecture of EIN through aseries of experiments, and compare the performance of the
proposed technique with that of state-of-the-art methods.
II. RELATED WORKS
Object detection is a fundamental topic in pattern recognition. Pedestrian detection based on boosted cascade classifiers was proposed by Snow [10], with Haar-like features
used to extract differences between frames. Although thisapproach is as fast as boosted cascade-based face detection,
it is not especially accurate.
Dalal et al. have proposed HOG features that capture
gradient information of small regions [1]. These features
are robust to variations in the appearance of pedestrians.
The SVM classifier is trained using HOG vectors. The
combination of HOG features and the SVM classifier has

Convolution

MaxoutLayer

Pooling
Layer Convolution

Maxout Pooling
Layer

Output Layer
Fully Connected Layer

Layer

Feature Extraction

Fig. 1.

Classiﬁcation

Conventional CNN architecture

become popular in the field of pedestrian detection. There
are many extensions to Dalal approach [3] [5] [4].
DPM archives high accuracy pedestrian detection that isrobust to pose variations [2]. It estimates probabilities acrossthe whole pedestrian region and some part regions at thesame time, and uses these to detect pedestrian regions. The
whole pedestrian model and each part region are basedon HOG features extracted from several resolutions. DPM
recorded the best performance in the pedestrian detection
benchmark in 2010. In addition, DPM has been applied topoint clouds captured from LIDAR [12]. Instead of HOG
features, this point cloud-based DPM uses the number ofpoints in each cell as a feature.
Conventional methods consist of feature extraction, which
is manually designed, and trained classification, which isbased on machine learning techniques. In particular, featuredesign requires human knowledge to ensure robustness. To
overcome this problem, deep learning-based approaches haveattracted attention. Deep learning, especially in CNN, has
excellent feature representation and classification abilities.
Krizhevsky has shown the potential of CNN in a 1000class object recognition benchmark (ILSVRC). After thisbreakthrough, Deep Learning approaches have been applied
to many problems, such as house number recognition, scenelabeling, and object detection. Ouyang et al. proposed theconcept of Joint Deep Learning, which is based on hierarchical neural networks. First, features are extracted from partial
pedestrian regions, and these are then combined hierarchically. Joint Deep Learning is robust to pose variations, andrecorded the top score in the Caltech pedestrian benchmark. R-CNN is one of the best detection approaches basedon Deep Learning [13]. This method determines regions ofinterest in an image using the superpixel method, and extracts
features using CNN. The extracted features are passed to an
SVM that is trained for each object and, finally, the specificobject position is detected. Although Joint Deep Learning
and R-CNN are capable of highly accurate pedestrian detection, their networks have a complicated structure.
III. C ONVOLUTIONAL N EURAL N ETWORKAs shown in Fig. 1, The CNN contains an alternate
succession of convolutional layers and Pooling layers. Thisis based on the notion of local receptive fields discovered by
Hubel [14]. There are several types of layers, including input
layers, convolutional layers, pooling layers, and classification
layers. Besides the raw data, each input layer also takes edges
and normalized data. The convolutional layer has M kernels

with size Kx×Ky that are filtered in order to input data. The
filtered responses from all the input data are then subsampled
in the pooling layer. Scherer [15] found that max pooling
can lead to faster convergence and improved generalization,
and Boureau [16] analyzed theoretical feature pooling. Max
Pooling outputs the maximum value in certain regions, suchas in a 2 × 2 pixel. Convolutional layers and pooling layers
are laid alternately to create the deep network architecture.
Finally, the classification layer outputs the probability of eachclass through a softmax connection of all weighted nodes in
the previous layer.
CNN utilizes supervised learning in which filters are
randomly initialized and updated through backpropagation[17]. The Backpropagation estimates the connected weights
and minimize E by gradient descent as shown in Eq. (1) andEq. (2).
N
1!
E=
En
(1)
2 n=1
W(l) ← W(l) + ∆W(l) = W(l) − η

∂En
∂W(l)

(2)

Note that {n|1, ..., N } is the training sample. η is thetraining ratio, W(l) is the weight that connects in layer lto the next layer (l + 1). The error for each training sample
En is the sum of the differences between the output value
and the label. ∆W(l) is represented as shown in Eq. (3).
∆W(l) = −ηδ (l) y(l−1)(3)δ (l) = eφ(A(l) )(4)A(l) = W(l) · y(l−1)(5)

y(l−1) is the output in the (l−1)th layer and e is the output
nodes error. A(l−1) is the accumulated value connected tonode from all nodes in the (l − 1)th layer. The local gradientdescent is obtained by Eq. (4). The activation function
φ may be a sigmoid, hyperbolic tangent, or ReLU [11].
The connected weights in the entire network are updated
concurrently for a predetermined number of iterations, oruntil some convergence condition is satisfied.
Dropout is an efficient method to reduce over-fitting andimprove generalization [8]. It randomly selects units witha probability of 50%. The features of the selected units
are then used for optimization during each iteration of the
training process. Dropout is also used for training in the fully
connected layer.
IV. P ROPOSED M ETHOD
We propose two techniques based on Dropout: Random
Dropout for the training process, and Ensemble Inference
Network (EIN) for the classification process. Details of thesetechniques are as follows.

0.23
0.77

0.42
0.58
MaxoutMaxout Pooling
Layer
Pooling
Convolution
Layer
Convolution
Layer
Layer

Negative

Positive

Median

0.23

0.77

Mean

0.26

0.73

Maximum

0.42

0.85

Selected decision mannerMedian, Mean, Maxixum...

0.15
0.85
Randomly select units whoes
response value is set to zeroFig. 3.

Convolution

MaxoutLayer

Pooling
Layer Convolution

Maxout Pooling

Layer

50％

50％

-%

50%

Output Layer

Layer
Fully Connected Layer

Dropout
Output of unit to zero

Algorithm of EIN.

50%

-%

50％

50%

-%

with ﬁxed probability

1 Iteration

2 Iteration

M Iteration

Random Dropout
Output of unit to zero

60％

30％

-%

40%

70%

-%

60％

50%

-%

with ﬁxed probability

1 Iteration

Fig. 2.

2 Iteration

M Iteration

Algorithm of conventional Dropout and Random Dropout.

A. Random Dropout
Conventional Dropout consists of setting the output ofeach hidden unit to zero with a fixed probability (usually50%). The hidden units that are set to zero are randomly
selected in each iteration. Dropout produces robustness byneglecting certain information from the layer below. We
extend this Dropout technique by applying a random probability in each iteration to give Random Dropout. Figure 2
illustrates the update process in conventional Dropout and the
proposed Random Dropout. Whereas conventional Dropout
always sets 50% of the units to zero, Random Dropout
applies a different ratio for each iteration. We first define

the Random Dropout probability as between 30% and 70%.
We set this ratio to 60% and 30% for each layer in the first
iteration, then change this to 40% and 70% for each layer inthe second iteration. For example, we set this ratio to 60%
and 30% for each layer in the first iteration, then change
this to 40% and 70% for each layer in the second iteration.
We aim to obtain generalization using a flexible Random
Dropout ratio.
B. Classifier using EIN
EIN is intended to remove connections from the previous
layer, similar to Dropout, in the classification process. We
randomly select units whose response value is set to zerowith 50% probability. The classification process of conventional CNN feeds the input values to the trained network
immediately. Unlike conventional CNN, EIN feeds the input
value to the fully connected layers of the trained network
at different times. At each time, some units of the fully
connected layers are randomly set to zero. In other words, ourmethod forms different networks from the original trained
network by randomly selecting different units. We compare
the decision manner based on median, mean or maximum
for pedestrian detection independently in experiments. The
steps of the EIN process are as follows.
1) Feature maps: First, the input image I is convoluted
with filter V, and feature maps are generated with activation
function φ, as shown in Eq. (6).
"#h = φ VT I + b(6)Here, b is a bias term. We employ the Maxout activation
function [9], which selects the maximum value from K
feature maps at each unit, as shown in Eq. (7).
h′i = max hikk∈[1,K](7)

We utilize max pooling to select the maximum value in eachcertain region, and obtain robustness against small shapevariations by resizing the feature map, as shown in Eq. (8).
h′′i = max h′p
p∈Pi

(8)

Although units in the fully connected layer will change
according to the random selection, the convolution layer andpooling layer remain the same. Thus, we can share the output
values from the final pooling layer.
2) Structured by some networks: The response of randomly selected units is set to zero in the fully connected
layer and classification layer, as shown in Eq.(9).
$
%
(l)
(l)
hj = φ W(l) x + b(l) · mj

(9)Note that the feature map obtained in Step 1 is defined asx. Wl and bl are the connection weights and bias of thelth layer, respectively. m controls the response value hlj , andis set to 0 or 1. When m is set to 0, the response value
is 0, and when m is set to 1, the response value is 1. We
construct N networks with randomly selected zero-responseunits. The probability of each class in network n is given by
the soft-max function in Eq. (10).
$%(L) (L)(L)
exp Wj hj + bj$%
Onc = &(L) (L)
(L)
C
exp
W
h
+
b
c
c
c
c=0

(10)

3) Finaly output Classifier value: The final output classis obtained from the probability Onc for each class in each
network. All Onc stored into probability set sc and medianis selected SMedianfrom them.
cV. P EDESTRIAN D ETECTION USING P ROPOSED M ETHOD
For pedestrian detection, we employ sliding window approach to detect multiple pedestrians at different scale. In
the detection process, it is necessary to distinguish whether
pedestrians are present or not in a huge number of windows.
We apply a HOG+SVM classifier to reduce the processing
time to find pedestrian candidate regions and the proposed
CNN is applied to them. The proposed CNN is applied topedestrian candidate windows.
VI. E XPERIMENTS
We evaluate the performance of Random Dropout andEIN using the Caltech Pedestrian Dataset and Daimler
Mono Pedestrian Benchmark Dataset. In addition, we evaluate the efficiency of Random Dropout and several EIN
architectures that have a different number of networks
and different final output decisions (maximum or average). We also compare the proposed method with, CNN,
HOG [1], HogLbp [3], LatSvm-V2 [2], VJ [10], DBNIsol [25], ACF [26], ACF-Caltech [26], Pls [20], FPDW [22],ChuFtrs [18], CrossTalk [19], RandomForest [5], MultiResC [21], Roerei [23], MOCO [27], Joint Deep [6]and Switchable Deep Network [28] for Caltech Pedestrian Dataset. For the Daimler Mono Pedestrian Benchmark

Input
Layer 1
Layer 2
Layer 3Layer 4Layer 5Layer 6
OutputTABLE IU SING CNN STRUCTURES
CaltechDataset108 × 36 × 3
Weight Filter
40, 9 × 5
Max Pooling
2×2
Maxout2
Weight Filter
64, 5 × 3
Max Pooling
2×2
Maxout2
Weight Filter
32, 3 × 3
Max Pooling
2×2
Maxout2# of Fully connected
1,000# of Fully connected
500# of Fully connected100
Softmax
2

Daimler
Dataset96 × 48 × 1
100, 5 × 3
2×2
280, 5 × 4
2×2
270, 5 × 4
2×2
2
1,000
500
100
2

Dataset, we compare with CNN, HOG [1], Shapelet [29],HogLbp [3], LatSvm-V2 [2], VJ [10], RandomForest [5],MultiFtr [30], and MLS [4].
The structures of CNN are shown in Table 1. The Caltech
Pedestrian Dataset has 4,000 positive samples and 200,000
negative samples for training, and 8,273 test images. We
augmented 101,808 positive samples from the originals byshifting, rotation, mirroring, and scaling. For the Daimler Mono Pedestrian Benchmark Dataset, we augmented
250,560 positive samples from 31,320 samples; the dataset
also has 254,356 negative samples. The total iteration of updating parameters is 500,000 with 5 minibatchs and training
ratio η is set to 0.01.
A. Performance comparison with the number of networks
First, we consider the best architecture of EIN by changing
the number of networks and the determining way of final
output. We evaluate the architectures of EIN that containnetwork from 1 to 33. Each network retained the samestructure in the convolutional layer and pooling layer, but
the fully connected layers varied according to the random
selection of units. When the number of networks is 1, we
have a conventional network. We select the mean probability
from all networks. Figures 4(a) and (b) show the evaluation
results using the Caltech Pedestrian Dataset and Daimler
Mono Pedestrian Benchmark Dataset, respectively. First, thedifference between the solid and broken lines indicates theperformance of Dropout and Random Dropout. In Caltech
Pedestrian Dataset case, we can see that Random Dropout
improves the accuracy by about 6%. The architecture that has
33 networks with mean selection achieves the best accuracy,a miss rate of 37.77% with the Caltech Pedestrian Dataset.
From this evaluation, EIN with multiple networks and different structures in the fully connected layers obtains better
detection accuracy. In Daimler Mono Pedestrian Benchmark
Dataset, whereas the miss rate of CNN are 35.78%, ourmethods significantly improves to 31.34% at 33 ensemble
networks with mean decision manner.

47

50
Dropout + EIN(Median)Dropout + EIN(Mean)
Dropout + EIN(Max)
Random Dropout + EIN(Median)Random Dropout + EIN(Mean)
Random Dropout + EIN(Max)Dropout + EIN(Median)Dropout + EIN(Mean)
Dropout + EIN(Max)
Random Dropout + EIN(Median)Random Dropout + EIN(Mean)
Random Dropout + EIN(Max)

46
45

45

Miss Rate[%]Miss Rate[%]

44
43
42
41

40

40

35
39
38
37
5

10

15

20

25

30

30

5

0.9

0.8

0.8
0.7

0.7

VJ 94.73%
HOG 68.46%
HogLbp 67.77%
LatSvm-V2 63.26%
Pls 62.10%
FPDW 57.40%
ChuFtrs 56.34%
CrossTalk 53.88%
DBN-Isol 53.14%
ACF 51.36%
RandomForest 40.15%
MultiResC 48.45%
Roerei 48.35%
CNN 46.31%
MOCO 45.53%
ACF-Caltech 44.22%
Joint Deep Learning 39.92%
Switchable Deep Network 37.87%
RandomDropout+EIN 37.77%
0.001

0.01

0.6

Miss Rate

Miss Rate

0.2
0.000130

Performance comparison with the number of networks
1

0.3

25

(b) Daimler Mono Pedestrian Bemchmark Dataset

1

0.4

20

(a) Caltech Pedestrian Dataset

0.9

0.5

15

# of Fully connected network

Fig. 4.

0.6

10# of Fully connected network

0.5

0.4

0.3

0.1

1

10

0.2
0.0001

0.001

0.01

0.1

1

10

False Positive per Image

False Positive per Image(b) Daimler Mono Pedestrian Benchmark Dataset(a) Caltech Pedestrian Dataset

Fig. 5.

VJ 94.68%
Shapelet 93.78%
HOG 59.81%
MultiFtr 57.22%
HikSvm 55.03%
HogLbp 48.72%
LatSvm-V2 38.00%
CNN 32.50%
RandomDropout+EIN 31.34%
RandomForest 29.40%
MLS 28.43%

Compare proposed methods and conventional methods

B. Performance comparison with conventional methods
We compare our method with state-of-the-art methods using the Caltech Pedestrian Dataset. As shown in Figure 5(a),with an FPPI of 0.1, our method gives a 10.5% increase inaccuracy compared to conventional CNN. In addition, theproposed method achieves similar performance to Switchable Deep Network, which constructs a complex network
structure. This demonstrates that a simple network architecture is sufficient to achieve state-of-the-art performance in
Deep Learning methods. We show a comparison result on
Daimler Mono Pedestrian Benchmark Dataset in Figure 5(b).
FPPI is improved 2.0% accuracy than conventional CNN at
0.1, compared to CNN making the current Daimler Mono
Pedestrian Benchmark Dataset top performance, it improvesdetection accuracy about 31.32% and archives comparable
performance to state-of-the-art methods.
Figure 6 shows a pedestrian detection result in Caltech
Pedestrian Dataset and Daimler Mono Pedestrian Benchmark

Detection. The results in the first and fourth columns areexamples of detection by HOG+SVM, second and fifth
columns are examples of detection by DPM. The pedestrian
detection result in the third and sixth columns are result of
the proposed methods. As shown Figure 6, while conventional methods does not detect small pedestrians, proposed
method detects them in same scene. The proposed methods
are capable to detect hard situation such as occlusion, various
poses. Moreover, it can reduce the false positive.
VII. C ONCLUSION
We proposed two techniques for improvement of pedestrian detection by random selection of the units based on the
Dropout. Random Dropout that is randomly setting corresponding value of the units to zero with flexible rate. EIN
constructs multiple networks that have different structure in
fully connected layer with decision manner of final output.
We achieve the comparable performance to state-of-the-art
methods in Deep Learning approach, even the structure ofHOG+SVM

DPM

Proposed Method(a) Caltech Pedestrian Dataset

HOG+SVM

DPM

Proposed Method(b) Daimler Mono Pedestrian Benchmark Dataset

Fig. 6.

Visualized pedestrian detection

proposed methods are significant simple. In future work,
we will try to reduce the computational cost for real timeprocessing.
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