Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 111-119
Published online June 25, 2018
https://doi.org/10.5391/IJFIS.2018.18.2.111
© The Korean Institute of Intelligent Systems
Youngwan Cho, and Kisung Seo
Department of Computer Engineering, Seokyeong University, Seoul, Korea
Correspondence to :
Kisung Seo (ksseo@skuniv.ac.kr)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
For detecting a pedestrian by using features of images, it is generally needed to establish a reference model that is used to match with input images. The support vector machine (SVM) or AdaBoost Cascade method have been generally used to train the reference pedestrian model in the approaches using the histogram of oriented gradients (HOG) as features of the pedestrian model. In this paper, we propose a new approach to match HOG features of input images with reference model and to learn the structure and parameters of the reference model. The Gaussian scoring method proposed in this paper evaluates the degree of feature coincidence with HOG maps divided with angle of the HOG vector. We also propose two approaches for leaning of the reference model: genetic algorithm (GA) based learning and genetic programming (GP) based learning. The GA and GP are used to search the best parameters of the gene and nonlinear function representing feature map of pedestrian model, respectively. We performed experiments to verify the performance of proposed method in terms of accuracy and processing time with INRIA person dataset.
Keywords: HOG model, Genetic algorithm, Genetic programming, Learning, Pedestrian detection
Machine vision has attracted much attention in recent decades of years because it is important for various application areas such as surveillance, car safety, and robotics. Recently, various researches on machine vision have been actively applied smart vehicles for driving safety and autonomous driving. The recent progresses in smart vehicles have growing needs to be endowed with pedestrian detection. This study aims at propose an approach to pedestrian detection with enhanced accuracy and reduced processing time.
In recent years, the number of approaches to detect pedestrians in monocular images has grown steadily. Benenson et al. [1] categorized the main paradigms for pedestrian detection into Viola-Jones variants, HOG+SVM rigid templates, deformable part detectors (DPM), and convolutional neural networks. Viola and Jones [2] recognized objects by combining Haar wavelet-like features. Instead of recognizing an object using a single classifier, they used several multiple cascade classifiers to recognize the object. Viola et al. [3] also developed a superior classifier progressively through a boosting method for detector training. Dollar et al. [4] evaluated that the ideas of Viola et al. [3] had served as a foundation for modern detectors. YOLO [6] and faster R-CNN [7] are currently considered as the state of the art techniques where real-time deep learning techniques are applied.
Dalal and Triggs [8] created the HOG, which is a method to generate a histogram by accumulating the magnitude in terms of angles of gradient and recognize the shape of the object through it. In addition, they used SVM as a learning machine to utilize the HOG features in detecting pedestrians. The DPM of Felzenszwalb et al. [9] utilized the method of Dalal and Triggs [8] as a building block. Zhu et al. [11] improved the speed of calculation in the study of Dalal and Triggs [8] by applying the cascade technique to HOG features. There is a difference between the conventional HOG and cascade HOG in that the cascade HOG transforms the size of the model variably and selects an excellent model among the converted models through the boosting learning, whereas the conventional HOG recognizes the object by fixing the size of the model.
Since their introduction, the number of variants of HOG features has proliferated greatly with nearly all modern detectors utilizing them in some form. Maji et al. [12] considered fast approximations of non-linear kernels. Dollar et al. [5] recognized pedestrians by utilizing various channels such as color and edge, unlike the HOG using a gray channel. Felzenszwalb et al. [10] determined the similarity between the whole model and partial model of the pedestrian model after their generation and finally judged whether it is a pedestrian or not. In most of studies using the HOG such as studies of Dalal and Triggs [8] and Felzenszwalb et al. [10], pedestrian recognition is carried out by using learning based approach in which discriminative features are extracted from each candidate and then, they are passed through the learning machine or classifier such as SVM. In this paper, we propose a new approach for matching and training of pedestrian model using the HOG features.
In this paper, we use a matching technique using the pedestrian HOG feature model as a method for pedestrian detection through a vision sensor. After distinguishing features and similarities in the input image with the pedestrian HOG feature model, the detector judges whether it is a pedestrian or not. To determine the similarities, we propose a scoring method using the Gaussian function in this paper. The scoring method divides the HOG features by each angle, accumulates the score values calculated at each angle and obtains the final score.
If the pedestrian HOG feature model is configured with a single pedestrian image, there might be caused errors that detect only a specific pedestrian by characteristics such as clothing, hair style and posture of the pedestrian. Therefore, it is necessary to train the pedestrian HOG feature model to express the generalized pedestrian characteristics. The HOG feature model is learned using genetic algorithm. In addition, we use genetic programming to learn the nonlinear function expressing the reference model which is used to calculate the final score value to determine the detection of pedestrian.
In this research, we divide the HOG feature values ranging from 0° to 360° degrees by the angle and determine the similarity between the model and an input image with respect to the feature values on each divided angle space. This paper proposes a Gaussian scoring to determine the degree of similarity for each angle as given in
where,
where
When the scoring based decision is applied to pedestrian detection, a huge amount of calculation is needed because the scoring is performed through the exponential function for all cells. Therefore, a lookup table is used to improve the calculation speed.
Since the value of the Gaussian function is determined by the ratio of the HOG value of the input image to the reference model as shown in
The scoring function (
where
GA was first developed by John Holland in 1975 as an intelligent algorithm that solves the optimization problems based on Darwin’s theory of evolution. It was developed by mimicking the biological evolution and explores the best genes through crossover, mutation and selection operations. Many studies have reported that GA and GP provide an efficient and robust alternative for solving complex and highly nonlinear optimization problems utilizing global search procedures. The GA and GP model and their optimization procedures can be applied to solve the problem of establishing and training of the reference pedestrian model because it belongs to highly nonlinear optimization problem.
This paper proposes a HOG model training method using GA and applies the model to detection of general pedestrian characteristics. The INRIA person dataset is used for the training image of the HOG model, and the pedestrian HOG model is trained using 1, 216 pedestrian images and 1, 218 background images. The genetic configuration of the pedestrian model is shown in Figure 1.
In order to apply GA to learn the HOG pedestrian detection model, the model image and training images need to be presented as genes. A pedestrian model image is divided into cells as shown in Figure 1. A gene represented by
GA needs to evaluate the fitness of gene to learn the parameter of the model. In order to construct the fitness function, we define the average score of the pedestrian images and non-pedestrian images. The average score is obtained by averaging the Gaussian scorings of the whole image of the pedestrian and non-pedestrian, respectively, with the GA HOG model as shown in Figure 2 and
In
It can be noted that the high value of
In graphs of Figure 3,
where
The overall flow of GP algorithm [13] is similar to GA, but the structure of genes is different from each other. While GA has a fixed structure due to its bit string or real-number string, GP has a variable structure as the genes are expressed in a tree structure. Since the genetic structure of GP is variable, it has been widely applied to practical optimization such as searching for complex nonlinear functions or designing the system model itself.
The cell score consists of average value of angle scores corresponding to the scores for each angle in
We can train cell score function of
Through the Gaussian scoring of INRIA person dataset and GA HOG learning model introduced for learning the score, the angle score is obtained as shown in Figure 4, and it is utilized as a GP terminal.
Based on the angle score value obtained as in Figure 4, a GP tree is configured to obtain the final score. In order to evaluate the fitness of the GP tree, the average score
GP fitness is similar to the fitness of GA HOG model learning. As in the GA model, the larger the difference between
In order to verify the performance of the scoring learning technique using GP and pedestrian HOG model learning using GA and Gaussian scoring proposed in this paper, the pedestrian detection test was performed with the testing image of INRIA person dataset. We used ACC (Accuracy), CSI (Critical Success Index) and PAG (Post Agreement) as the performance indexes for the experiment of pedestrian image detection. Table 2 shows the result division table for performance index parameters, and the performance indexes are calculated with the parameters as shown in
We verify the performance of pedestrian detection through GA HOG model and GP scoring and compare the performance with a pedestrian classifier using SVM of HOG features. The parameters used in GA learning are summarized in Table 3, and Figure 7 shows the average score of the best GA HOG model.
We experimented GP learning with the parameters given in Table 4, and the result of best GP are shown in Figure 8. Table 5 shows a summary of the analysis on the performance comparison of the pedestrian detection using the conventional SVM and the algorithm proposed in this paper.
In Table 6, GS represents the Gaussian scoring, and GU means the Gaussian lookup table. The detection accuracy of GS and GA model was about 4% better than that of SVM, and the performance of GS+GA+GP model showed about 9% better in accuracy compared to SVM. In addition, the execution time of the proposed methods was measured, and the results of the time taken to execute a single input image with a resolution of 640 × 480 are summarized in Table 6. All the algorithms proposed in this paper are much better than SVM in terms of the execution time, and the execution time of the algorithm using the Gaussian lookup table proposed in this paper was about 2.3 times faster than the conventional GS.
This paper proposed a Gaussian Scoring method for matching images and GA/GP based learning to establish a reference model for pedestrian detection.
In the conventional approaches using HOG feature for matching, the HOG feature map consists of vectors representing the magnitude and angle of gradient for each pixel, and a reference model is trained with learning algorithms such as SVM and AdaBoost cascade method. In this paper, we consider the HOG value separately for each divided angle and evaluate the degree of HOG feature coincidence using the GS. By evaluating the HOG feature for each divided angle, it allowed us to visualize the features and make it easy to analyze it. This has a major advantage over the conventional HOG matching technique that cannot ensure the visualized representation due to large dimension of the HOG vector. In order to solve the problem of processing time to calculate the Gaussian score for all cells of the input images, we proposed a method to improve the processing time by using the Gaussian lookup table. In this paper, we also proposed GA and GP based training methods for the reference model. The GA and GP are used to search the best parameters of the gene and nonlinear function representing feature map of pedestrian model, respectively.
We performed experiments to verify the performance of proposed method in terms of accuracy and processing time with INRIA person dataset. The experimental results showed us that the accuracies of pedestrian detection using GA model and GP model were about 4% and 9% higher, respectively than the SVM method. In terms of processing time, the results showed us that the GA model using Gaussian lookup table has the best performance and 7.8 times faster than the SVM model. For future works, it is challenged to devise a model and training algorithm for recognizing the pedestrian even from input images where various size of pedestrian is appeared or a part of pedestrian is covered by something.
No potential conflict of interest relevant to this article was reported.
This research was supported by Seokyeong University in 2018.
No potential conflict of interest relevant to this article was reported.
Table 1. Configuration of GP terminal and function.
Node | Arity | Description |
R | 0 | Arbitrary values ranging from 0 to 1 |
angle score_{0~340} | 0 | Score values of each angle |
|Sin| | 1 | Absolute value of Sin function |
|Cos| | 1 | Absolute value of Cos function |
+ | 2 | Sum of two channels |
|−| | 2 | Absolute value of difference between two channels |
* | 2 | Product of two channels |
/ | 2 | Division of two channels |
Avg | 2 | Average of two channels |
Wf1 | 2 | Weighted sum of two channels |
Wf2 | 2 | Weighted sum of two channels |
Max | 3 | Maximum value among three channels |
Min | 3 | Minimum value among three channels |
Table 2. Results division table.
Predicted | |||
---|---|---|---|
Yes | No | ||
Actual | Yes | True Positive | False Negative |
No | False Negative | True Negative |
Table 3. GA parameter values.
Parameter | Value |
---|---|
Population size | 50 |
Max generation | 100 |
Crossover rate | 0.9 |
Mutation rate | 0.1 |
Select method | Tournament (size= 7) |
Table 4. GP parameter values.
Parameter | Value |
---|---|
Population size | 1, 000 |
Max generation | 200 |
Crossover rate | 0.9 |
Mutation rate | 0.1 |
Select method | Tournament (size= 7) |
Initial depth | 6–8 |
Max depth | 17 |
Initial population | Half and half |
Table 5. Experimental results of pedestrian detection performance.
Efficiency index | |||
---|---|---|---|
ACC | PAG | CSI | |
SVM | 0.847 | 0.801 | 0.724 |
GS | 0.743 | 0.752 | 0.592 |
GS + GA | 0.890 | 0.889 | 0.802 |
GS + GA + GP | 0.927 | 0.925 | 0.871 |
Table 6. Comparison of execution time.
Execution time (FPS) | |
---|---|
GS + GA | 44 ms |
GS + GA + GP | 53 ms |
SVM | 148 ms |
GU + GA | 19 ms |
GU + GA + GP | 28 ms |
E-mail: ywcho@skuniv.ac.kr
E-mail: ksseo@skuniv.ac.kr
Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 111-119
Published online June 25, 2018 https://doi.org/10.5391/IJFIS.2018.18.2.111
Copyright © The Korean Institute of Intelligent Systems.
Youngwan Cho, and Kisung Seo
Department of Computer Engineering, Seokyeong University, Seoul, Korea
Correspondence to:Kisung Seo (ksseo@skuniv.ac.kr)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
For detecting a pedestrian by using features of images, it is generally needed to establish a reference model that is used to match with input images. The support vector machine (SVM) or AdaBoost Cascade method have been generally used to train the reference pedestrian model in the approaches using the histogram of oriented gradients (HOG) as features of the pedestrian model. In this paper, we propose a new approach to match HOG features of input images with reference model and to learn the structure and parameters of the reference model. The Gaussian scoring method proposed in this paper evaluates the degree of feature coincidence with HOG maps divided with angle of the HOG vector. We also propose two approaches for leaning of the reference model: genetic algorithm (GA) based learning and genetic programming (GP) based learning. The GA and GP are used to search the best parameters of the gene and nonlinear function representing feature map of pedestrian model, respectively. We performed experiments to verify the performance of proposed method in terms of accuracy and processing time with INRIA person dataset.
Keywords: HOG model, Genetic algorithm, Genetic programming, Learning, Pedestrian detection
Machine vision has attracted much attention in recent decades of years because it is important for various application areas such as surveillance, car safety, and robotics. Recently, various researches on machine vision have been actively applied smart vehicles for driving safety and autonomous driving. The recent progresses in smart vehicles have growing needs to be endowed with pedestrian detection. This study aims at propose an approach to pedestrian detection with enhanced accuracy and reduced processing time.
In recent years, the number of approaches to detect pedestrians in monocular images has grown steadily. Benenson et al. [1] categorized the main paradigms for pedestrian detection into Viola-Jones variants, HOG+SVM rigid templates, deformable part detectors (DPM), and convolutional neural networks. Viola and Jones [2] recognized objects by combining Haar wavelet-like features. Instead of recognizing an object using a single classifier, they used several multiple cascade classifiers to recognize the object. Viola et al. [3] also developed a superior classifier progressively through a boosting method for detector training. Dollar et al. [4] evaluated that the ideas of Viola et al. [3] had served as a foundation for modern detectors. YOLO [6] and faster R-CNN [7] are currently considered as the state of the art techniques where real-time deep learning techniques are applied.
Dalal and Triggs [8] created the HOG, which is a method to generate a histogram by accumulating the magnitude in terms of angles of gradient and recognize the shape of the object through it. In addition, they used SVM as a learning machine to utilize the HOG features in detecting pedestrians. The DPM of Felzenszwalb et al. [9] utilized the method of Dalal and Triggs [8] as a building block. Zhu et al. [11] improved the speed of calculation in the study of Dalal and Triggs [8] by applying the cascade technique to HOG features. There is a difference between the conventional HOG and cascade HOG in that the cascade HOG transforms the size of the model variably and selects an excellent model among the converted models through the boosting learning, whereas the conventional HOG recognizes the object by fixing the size of the model.
Since their introduction, the number of variants of HOG features has proliferated greatly with nearly all modern detectors utilizing them in some form. Maji et al. [12] considered fast approximations of non-linear kernels. Dollar et al. [5] recognized pedestrians by utilizing various channels such as color and edge, unlike the HOG using a gray channel. Felzenszwalb et al. [10] determined the similarity between the whole model and partial model of the pedestrian model after their generation and finally judged whether it is a pedestrian or not. In most of studies using the HOG such as studies of Dalal and Triggs [8] and Felzenszwalb et al. [10], pedestrian recognition is carried out by using learning based approach in which discriminative features are extracted from each candidate and then, they are passed through the learning machine or classifier such as SVM. In this paper, we propose a new approach for matching and training of pedestrian model using the HOG features.
In this paper, we use a matching technique using the pedestrian HOG feature model as a method for pedestrian detection through a vision sensor. After distinguishing features and similarities in the input image with the pedestrian HOG feature model, the detector judges whether it is a pedestrian or not. To determine the similarities, we propose a scoring method using the Gaussian function in this paper. The scoring method divides the HOG features by each angle, accumulates the score values calculated at each angle and obtains the final score.
If the pedestrian HOG feature model is configured with a single pedestrian image, there might be caused errors that detect only a specific pedestrian by characteristics such as clothing, hair style and posture of the pedestrian. Therefore, it is necessary to train the pedestrian HOG feature model to express the generalized pedestrian characteristics. The HOG feature model is learned using genetic algorithm. In addition, we use genetic programming to learn the nonlinear function expressing the reference model which is used to calculate the final score value to determine the detection of pedestrian.
In this research, we divide the HOG feature values ranging from 0° to 360° degrees by the angle and determine the similarity between the model and an input image with respect to the feature values on each divided angle space. This paper proposes a Gaussian scoring to determine the degree of similarity for each angle as given in
where,
where
When the scoring based decision is applied to pedestrian detection, a huge amount of calculation is needed because the scoring is performed through the exponential function for all cells. Therefore, a lookup table is used to improve the calculation speed.
Since the value of the Gaussian function is determined by the ratio of the HOG value of the input image to the reference model as shown in
The scoring function (
where
GA was first developed by John Holland in 1975 as an intelligent algorithm that solves the optimization problems based on Darwin’s theory of evolution. It was developed by mimicking the biological evolution and explores the best genes through crossover, mutation and selection operations. Many studies have reported that GA and GP provide an efficient and robust alternative for solving complex and highly nonlinear optimization problems utilizing global search procedures. The GA and GP model and their optimization procedures can be applied to solve the problem of establishing and training of the reference pedestrian model because it belongs to highly nonlinear optimization problem.
This paper proposes a HOG model training method using GA and applies the model to detection of general pedestrian characteristics. The INRIA person dataset is used for the training image of the HOG model, and the pedestrian HOG model is trained using 1, 216 pedestrian images and 1, 218 background images. The genetic configuration of the pedestrian model is shown in Figure 1.
In order to apply GA to learn the HOG pedestrian detection model, the model image and training images need to be presented as genes. A pedestrian model image is divided into cells as shown in Figure 1. A gene represented by
GA needs to evaluate the fitness of gene to learn the parameter of the model. In order to construct the fitness function, we define the average score of the pedestrian images and non-pedestrian images. The average score is obtained by averaging the Gaussian scorings of the whole image of the pedestrian and non-pedestrian, respectively, with the GA HOG model as shown in Figure 2 and
In
It can be noted that the high value of
In graphs of Figure 3,
where
The overall flow of GP algorithm [13] is similar to GA, but the structure of genes is different from each other. While GA has a fixed structure due to its bit string or real-number string, GP has a variable structure as the genes are expressed in a tree structure. Since the genetic structure of GP is variable, it has been widely applied to practical optimization such as searching for complex nonlinear functions or designing the system model itself.
The cell score consists of average value of angle scores corresponding to the scores for each angle in
We can train cell score function of
Through the Gaussian scoring of INRIA person dataset and GA HOG learning model introduced for learning the score, the angle score is obtained as shown in Figure 4, and it is utilized as a GP terminal.
Based on the angle score value obtained as in Figure 4, a GP tree is configured to obtain the final score. In order to evaluate the fitness of the GP tree, the average score
GP fitness is similar to the fitness of GA HOG model learning. As in the GA model, the larger the difference between
In order to verify the performance of the scoring learning technique using GP and pedestrian HOG model learning using GA and Gaussian scoring proposed in this paper, the pedestrian detection test was performed with the testing image of INRIA person dataset. We used ACC (Accuracy), CSI (Critical Success Index) and PAG (Post Agreement) as the performance indexes for the experiment of pedestrian image detection. Table 2 shows the result division table for performance index parameters, and the performance indexes are calculated with the parameters as shown in
We verify the performance of pedestrian detection through GA HOG model and GP scoring and compare the performance with a pedestrian classifier using SVM of HOG features. The parameters used in GA learning are summarized in Table 3, and Figure 7 shows the average score of the best GA HOG model.
We experimented GP learning with the parameters given in Table 4, and the result of best GP are shown in Figure 8. Table 5 shows a summary of the analysis on the performance comparison of the pedestrian detection using the conventional SVM and the algorithm proposed in this paper.
In Table 6, GS represents the Gaussian scoring, and GU means the Gaussian lookup table. The detection accuracy of GS and GA model was about 4% better than that of SVM, and the performance of GS+GA+GP model showed about 9% better in accuracy compared to SVM. In addition, the execution time of the proposed methods was measured, and the results of the time taken to execute a single input image with a resolution of 640 × 480 are summarized in Table 6. All the algorithms proposed in this paper are much better than SVM in terms of the execution time, and the execution time of the algorithm using the Gaussian lookup table proposed in this paper was about 2.3 times faster than the conventional GS.
This paper proposed a Gaussian Scoring method for matching images and GA/GP based learning to establish a reference model for pedestrian detection.
In the conventional approaches using HOG feature for matching, the HOG feature map consists of vectors representing the magnitude and angle of gradient for each pixel, and a reference model is trained with learning algorithms such as SVM and AdaBoost cascade method. In this paper, we consider the HOG value separately for each divided angle and evaluate the degree of HOG feature coincidence using the GS. By evaluating the HOG feature for each divided angle, it allowed us to visualize the features and make it easy to analyze it. This has a major advantage over the conventional HOG matching technique that cannot ensure the visualized representation due to large dimension of the HOG vector. In order to solve the problem of processing time to calculate the Gaussian score for all cells of the input images, we proposed a method to improve the processing time by using the Gaussian lookup table. In this paper, we also proposed GA and GP based training methods for the reference model. The GA and GP are used to search the best parameters of the gene and nonlinear function representing feature map of pedestrian model, respectively.
We performed experiments to verify the performance of proposed method in terms of accuracy and processing time with INRIA person dataset. The experimental results showed us that the accuracies of pedestrian detection using GA model and GP model were about 4% and 9% higher, respectively than the SVM method. In terms of processing time, the results showed us that the GA model using Gaussian lookup table has the best performance and 7.8 times faster than the SVM model. For future works, it is challenged to devise a model and training algorithm for recognizing the pedestrian even from input images where various size of pedestrian is appeared or a part of pedestrian is covered by something.
No potential conflict of interest relevant to this article was reported.
This research was supported by Seokyeong University in 2018.
Genetic configuration of the pedestrian HOG model.
Calculation of the average score
Effect of score variance to detect pedestrian.
Angle score calculation process.
GP score average calculation process.
Threshold setting.
Best GA model.
Best GP model.
Table 1 . Configuration of GP terminal and function.
Node | Arity | Description |
R | 0 | Arbitrary values ranging from 0 to 1 |
angle score_{0~340} | 0 | Score values of each angle |
|Sin| | 1 | Absolute value of Sin function |
|Cos| | 1 | Absolute value of Cos function |
+ | 2 | Sum of two channels |
|−| | 2 | Absolute value of difference between two channels |
* | 2 | Product of two channels |
/ | 2 | Division of two channels |
Avg | 2 | Average of two channels |
Wf1 | 2 | Weighted sum of two channels |
Wf2 | 2 | Weighted sum of two channels |
Max | 3 | Maximum value among three channels |
Min | 3 | Minimum value among three channels |
Table 2 . Results division table.
Predicted | |||
---|---|---|---|
Yes | No | ||
Actual | Yes | True Positive | False Negative |
No | False Negative | True Negative |
Table 3 . GA parameter values.
Parameter | Value |
---|---|
Population size | 50 |
Max generation | 100 |
Crossover rate | 0.9 |
Mutation rate | 0.1 |
Select method | Tournament (size= 7) |
Table 4 . GP parameter values.
Parameter | Value |
---|---|
Population size | 1, 000 |
Max generation | 200 |
Crossover rate | 0.9 |
Mutation rate | 0.1 |
Select method | Tournament (size= 7) |
Initial depth | 6–8 |
Max depth | 17 |
Initial population | Half and half |
Table 5 . Experimental results of pedestrian detection performance.
Efficiency index | |||
---|---|---|---|
ACC | PAG | CSI | |
SVM | 0.847 | 0.801 | 0.724 |
GS | 0.743 | 0.752 | 0.592 |
GS + GA | 0.890 | 0.889 | 0.802 |
GS + GA + GP | 0.927 | 0.925 | 0.871 |
Table 6 . Comparison of execution time.
Execution time (FPS) | |
---|---|
GS + GA | 44 ms |
GS + GA + GP | 53 ms |
SVM | 148 ms |
GU + GA | 19 ms |
GU + GA + GP | 28 ms |
Zong Woo Geem, and Jin-Hong Kim
International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(4): 237-244 https://doi.org/10.5391/IJFIS.2018.18.4.237Jae Ho Park, Jung Suk Yu, and Zong Woo Geem
Int. J. Fuzzy Log. Intell. Syst. 2018; 18(2): 135-145 https://doi.org/10.5391/IJFIS.2018.18.2.135Heesung Lee, and Euntai Kim
Int. J. Fuzzy Log. Intell. Syst. 2015; 15(2): 96-101 https://doi.org/10.5391/IJFIS.2015.15.2.96Genetic configuration of the pedestrian HOG model.
|@|~(^,^)~|@|Calculation of the average score
Effect of score variance to detect pedestrian.
|@|~(^,^)~|@|Angle score calculation process.
|@|~(^,^)~|@|GP score average calculation process.
|@|~(^,^)~|@|Threshold setting.
|@|~(^,^)~|@|Best GA model.
|@|~(^,^)~|@|Best GP model.