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A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor
Kaushal Kumar| Ritesh Kumar Mishra*
Department of Electronics and Communication Engineering, National Institute of Technology, Patna, India
Corresponding Author Email:
ritesh@nitp.ac.in
https://doi.org/10.18280/ts.360110
| Citation
36.01_10.pdf
Pedestrian detection is a fundamental problem in various computer vision applications and is addressed by complex solutions involving complex feature extractor and classification techniques. In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time. A feature selector organizes the extracted features in decreasing order of their significance. A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability. The proposed scheme is found to have a 50 % reduction in feature size, 67 % reduction in processing time and 3.93 % improvement in accuracy.
classifier, feature selection, HOG, HSG, pedestrian detection, SVM
Robust detection of pedestrian with complex background is a challenging task in computer vision. It helps in a number of applications including smart vehicle, smart surveillance system and human computer interaction. The challenge in pedestrian detection is that the algorithm should provide accurate detection under different illumination conditions. Moreover, due to various poses and variation in clothing that human exhibit makes it a difficult problem in computer vision. For its real time implementation, the algorithm should provide accurate solution in short processing time. All these reasons make pedestrian detection an active area of research. Feature extraction along with classifier is the most common approach followed in pedestrian detection.
Histogram of Oriented Gradients (HOG) in association with linear Support Vector Machine (SVM) classifier is regarded as a better candidate than other single features proposed for detecting pedestrian [1-2]. Bilal et al. [3] proposed a modified version of HOG termed as HSG which has less algorithmic complexity than HOG. In this paper, we have further improved the detection capability of HSG by appending feature selection block. Addition of feature selector will also reduce processing time and results in a robust feature selector. The framework for detecting pedestrian used in this paper is shown in Figure 1.
1b.png
Figure 1. Block diagram of pedestrian detection framework: (a) Training phase (b) Testing phase
It is found that HSG descriptor with linear SVM classifier and mRMR feature selector provides better performance than the approach used without feature selector. The rest of the paper is categorized as follows: Section 2 provides a brief background on research work done in pedestrian detection. Section 3 describes the preliminary work required in this paper. Section 4 provides an outlook into the proposed approach. Section 5 compares the result of the proposed approach with existing method. Section 6 concludes the work focusing important findings.
Now a days, a significant attention has been brought by the computer vision researchers to solve the problem of pedestrian detection in images and videos. Many studies are going on how to effectively extract the pedestrian features. SIFT [4-5], SURF [6], LBP [7-9], HOG[10], EOH [11] and SC [12] are some of the commonly used techniques of feature extraction. Among all such techniques, HOG is found to outperform others in detecting pedestrian. Dalal and Triggs proposed HOG for human detection [10]. HOG is a shape descriptor and remains unaffected by illumination and rotation transformation. Lowe D G et al. initiated Scale Invariant Feature Transform (SIFT) identify key points based on the difference of gaussian (DoG) [4]. SIFT is similar to HOG because in SIFT, the gradient is calculated in patches and in HOG, gradient is calculated over image. SIFT is therefore used for small scale detection rather than HOG which is preferred for large scale target detection. SIFT is hindered by high computational complexity. The high computation has been reduced by Speeded-Up Robust Features (SURF), initiated by Bay et al. [6]. SURF uses box filters for decreasing SIFT features and thus reducing the computational complexity. Viola P et al. recommended Haar wavelet for pedestrian detection, which uses rectangular feature similar to SURF [13] but is not preferable for complex background. Belongie et al. proposed shape context (SC) for object detection, which uses two dimensional histogram to build from edge pixels of image and finding the similarity between histograms of given image and prototype image [12]. SC uses edge information rather than their orientation. However, results are not satisfactory with complex background and also it takes a lot of processing time. Ojala et al. proposed LBP descriptor that provides textural features of an image which is used for pedestrian detection [9]. It is also used for image segmentation [14-15] and face detection [16]. The object characteristics are described by textural information. LBP has been extended by various researchers for increasing its performance and achieving specific requirements such as Rotation In-variant LBP [17-18] and Elongated Local Binary Patterns (ELBP) [19]. Mu Y et al. recommended another types of LBP such as Semantic-LBP and Fourier-LBP which is employed in human recognition and detection [7]. For improving the accuracy of detecting pedestrian, wang et al. combined both HOG and LBP descriptors [20]. Lie et al. recommended a similar type of combination for detecting pedestrian based on multi pose features [21]. Xin et al. proposed a technique combining Haar and HOG features [22]. In [23], HOG, Haar and SC are combined for improving the performance of the detector.
Classifier also plays an important role in deciding detector's accuracy. Initially, linear SVM has been used with HOG descriptor for feature classification [10]. Linear SVM is commonly used because of its simplicity in manipulation. Maji et. al. used another version of SVM termed as Histogram Intersection Kernel (HIK) which is employed with edge energy based features [24]. They reported an improvement of 13 % over HOG-linear SVM on INRIA dataset. In [25-26], AdaBoost classifier is used which improves the detection accuracy by 10 %. AdaBoost is preferred for object detection employing cascade classification and various researchers [27] employed this approach. In summary, HOG comes out to be better detector than others for pedestrian detection and researchers are combining it with other techniques under different scenarios. SVM and AdaBoost are the popular classifiers used for pedestrian detection because of improved performance.
This section presents an overview of HSG descriptor [3] used for describing the shape of pedestrian in an image. The HSG descriptor is a modified version of HOG. The complexity of HSG algorithm is reduced compared to HOG without sacrificing its accuracy. HOG descriptor is deduced from EOH, which is found to have better accuracy. HOG has been initiated by Dalal and Triggs [10] for detecting human. In HOG, the image gradient is calculated for every pixel along x and y direction which gives information about color changes. Let P(x,y) gives information about pixel color at (x,y), then its gradient vector is given by Eq. (1).
$\nabla P(x,y)=\left[ \begin{matrix} {{\sigma }_{x}} \\ {{\sigma }_{y}} \\\end{matrix} \right]=\left[ \begin{matrix} \frac{\partial \sigma }{\partial x} \\ \frac{\partial \sigma }{\partial y} \\\end{matrix} \right]=\left[ \begin{matrix} \sigma (x+1,y)-\sigma (x-1,y) \\ \sigma (x,y+1)-\sigma (x,y-1) \\ \end{matrix} \right]$ (1)
where, $∂σ/∂x$ represents first order partial derivative along x-direction, computed by taking the difference between pixels on the right and left of the target. Similarly, $∂σ/∂y$ is computed by differencing the pixels above and below the target. From image gradient, the magnitude and orientation of each pixel can be computed which is given in Eq. (2) and (3).
${{P}_{mag}}=\sqrt{\sigma _{x}^{2}\,+\sigma _{y}^{2}\,}$ (2)
$\theta =\arctan \left( {}^{{{\sigma }_{y}}}/{}_{{{\sigma }_{x}}} \right)$ (3)
The step by step procedure of feature extraction using HSG descriptor from input image is discussed next.
Step 1: Input an image frame.
Step 2: Divide the frame into a detection window of 64x128 with a stride of 8.
Step 3: Divide the detection window into 8x8 blocks with a stride of 4.
Step 4: Compute the gradient magnitude and direction for each pixel present in the block. This process is similar to HOG.
Step 5: Compute the average over 8x8 block. This average is taken as threshold value. Let M(b1) represents the mean over first 8x8 block given by Eq. (4).
$M(b1)=(P_mag (x1,y1)+P_mag (x2,y2)+⋯…+P_mag (x8,y8))/64$ (4)
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