Histogram of Oriented Phase and Gradient (HOPG) Descriptor for Improved Pedestrian Detection
This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG) The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more robust and less sensitive descriptors to lighting variations. The phase congruency information and the gradient of each pixel in the image are extracted with respect to its neighborhood. Histograms of the phase congruency and the gradients of the local segments in the image are computed with respect to its orientations. These histograms are concatenated to construct the HOPG descriptor. The performance evaluation of the proposed descriptor was performed using INRIA and DaimlerChrysler datasets. A linear support vector machine (SVM) classifier is used to train the pedestrians. The experimental results show that the human detection system based on the proposed features has less error rates and better detection performance over a set of state of the art feature extraction methodologies. Introduction Pedestrian detection is one of the widely used applications in the pattern recognition and computer vision systems. Over the last decade, detection of human beings in a visual surveillance system is a significant task due to its extended applications including human computer interaction, person identification, event detection, counting people in crowded regions, gender classification, automatic navigation, safety systems, etc. The fluctuating appearance of the human body combined with the occlusions, cluttered scenes and illumination changes, make the pedestrian detection application as one of the challenging categories in object detection. This task has been tackled by different techniques. Histogram of Oriented Gradient (HOG) introduced in 2005 by Dalal and Triggs [1] is one of the most popular techniques used for the pedestrian detection features. In this approach, the distribution of the gradient information is used to depict the shape and the appearance of the objects. This can be achieved by scanning the local portions of the image and counting the occurrences of the gradient orientation. In the last decade, this method and other single features algorithms such as Scale Invariant Feature Transform (SIFT) [14] Local Binary Pattern (LBP) [19] Edgelet [20] Haar [21] and Shapelet [22] played vital role in the pedestrian detection systems. However they were lacking to deal efficiently with images impacted by the noise and the illumination variations. Histogram of Oriented Phase (HOP) descriptor based on phase congruency has been developed to eliminate such problems and improve the detection performance. This descriptor can be used to detect and localize the significant point features of the image better than the traditional gradient operators especially in the regions affected by illuminations and where the perceived edge does not have a sharp step or a large derivative. However HOP descriptor is sensitive to noise and it is not efficient to detect objects in such case. Although, the above mentioned single feature algorithms could be used to depict the appearance of the objects reasonably, however they are still not enough to deal efficiently with the complex scenes such as poor image resolution, occlusion, and crowded scenes. The efficiency limitation of the single features based techniques raised up the need of combined multi features to improve the performance of human detection. In 2007, Zhang and Ram [2] improved the detection of the IR images by the combination of the Edglets and HOG features. Wojek [3] combined HOG, Haar and shapelet features. Wang et al [4] connected HOG and LBP features to improve pedestrian detection performance. Dollar [5] also developed the integral channel features (ICF) that combined HOG, gradient magnitude and LUV color, etc. In this paper we propose a new feature extraction algorithm that combines the image shape features based on the local phase information with the histogram of oriented gradient features denoted as HOPG descriptor. This approach leads an extraction of the complementary information of the image, hence significantly increases the detection rate of the system. The pedestrian detection framework based on HOPG features is shown in Figure 1. Pedestrian NonPedestrian Figure 1. Framework of the pedestrian detection system based on HOPG features Gradient magnitude and orientation Linear SVM classifier Histograms concatenation HOPG Features Input image Fusing local region's histograms HOPG feature vectors of training datasets Phase congruency magnitude and orientation Histogram of Oriented Gradient HOG of local regions Histogram of Oriented Phase HOP of local regions The input image is divided into local regions. The gradients and phase congruency magnitudes and their orientations are computed in each pixel of the input image with respect to its neighborhood. The histogram of oriented gradient and the histogram of the oriented phase are computed in each local region and combined to each other. These histograms are concatenated to form the HOPG descriptor that representing the input sample. Thses features are fed to a support vector machine (SVM) classifier that should be trained first by the HOPG feature vectors of the pedestrian and non pedestrian training dataset. Based on these input data, the classifier will be able to decide whether the tested image belongs to the positive (pedestrian) or a negative (non pedestrian) category. The remaining sections of this paper are organized as following: Phase congruency concept and computation. Image gradient computation. Implementation of the Histogram of Oriented Phase and Gradient (HOPG) descriptor. Experimental results. Conclusion Phase congruency concept and computation Phase congruency PC is a technique developed by Kovesi [6] to detect features such as corners and edges of 2D digital images. The phase congruency algorithm postulates that features are perceived at points where the Fourier components are maximal in phase [6] [7] First, consider a 1D signal I(x) defined in the range [0,2p] This signal is represented by a sinusoidal in the frequency domain by applying the Fourier transform. Figure 2 shows that all the Fourier components meet in the edges where the PC is maximum. Since the Fourier components meet at the edges, the local energy E(x) would be also maximum at these points. Therefor the phase congruency is directly proportional to the energy [8] [9] [10] [15] The phase congruency becomes a dimensionless quantity by the normalization with the sum of all Fourier components amplitudes An [15] [18] Figure 2. The Fourier components of a step signal. Hence, the phase congruency PC is given by: PC(x) E(x) e An n (1) where e is a small number to avoid division by zero. Equation 1 shows that the peaks in the local energy correspond to the peaks in phase congruency [7] [11] It shows also that the phase congruency does not depend on the signal overall magnitude, making the features invariant to scale, illumination and contrast variations [7] Assume F(x) is the same as the input signal I(x) filtered from a DC component and FH(x) is 90 phase shift of F(x) (Hilbert Transform) Using F(x) and Hilbert Transform FH(x) The local energy function E(x) can be defined as given in Eq. 2 [18] E(x) F(x)2 FH(x) 2 (2) Calculation of phase congruency using Gabor filter Phase congruency can be computed by convolving the two dimensional signal with a pair of quadrature filters to extract the local frequencies and phase information. Log Gabor filter is an efficient band pass filter used in this paper to extract the local phase information spread over a wide spectrum. The transfer function of the Log Gabor filter is given by [16] G(o) ex p (log(o oo 2 2(log(k oo 2 (3) where oo is the center frequency of the filter. k oo is kept constant for various oo [8] [12] The transfer function of 2 D log Gabor filter constructed by using Gaussian function in the angular direction is given by: G(th) exp (th tho) 2 2sth 2 (4) where tho is the center orientation of the filter, and sth is the standard deviation of the Gaussian function in angular direction [8] [12] consider Mno o and Mne e are the odd symmetric and even symmetric components of the LogGabor filter at scale n and orientation o so that they form a quadrature pair. The response vector at scale n and orientation o is obtained by the convolution of each quadrature pair with the input signal I(x, y) and is given by: [eno(x, y) ono(x, y) [I(x, y) Mno e I(x, y) Mno o (5) The amplitude of the response Ano and the phase angle psno at scale n and orientation o are given by: Ano (eno 2 (x, y) ono 2 (x, y) (6) psno(x, y) tan 1 on(x, y) en(x, y) (7) Referring to equation (2) F(x, y) and FH(x, y) for a 2D signal are given by: F(x, y) eno(x, y) n (8) FH(x, y) ono(x, y) n (9) Hence, phase congruency P(x, y) of the 2D signal can be computed over various scales and orientation as: 2p 0 PC(x, y) eno(x, y) n 2 ono(x, y) n 2 o e Ano(x, y) n 0 (10) The orientation angle ph(x, y) is given by ph(x, y) tan FH(x, y) F(x, y) (11) The level of phase congruency lies between the values 0 and 1. Figure 3 illustrates an example of phase congruency for an image at various illumination and contrast levels. We can note that the phase congruency is the same and invariant with illumination changes. Figure 3. Phase congruency of the image at various illumination