Implementation of a Pedestrian Detection Device based on CENTRIST for an Embedded Environment

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1 , pp Implementation of a Pedestrian Detection Device based on CENTRIST for an Embedded Environment Yun-Seop Hwang 1, Jae-Chang Kwak 2, Kwang-Yeob Lee 1 1 Dept. of Computer Engineering 2 Dept. of Computer Science Seokyeong University, Seoul, Korea vm3300@skuniv.ac.kr Abstract. This paper proposes implementation of CENTRIST-based pedestrian detection in embedded environments. Although a considerable number of pedestrian detection algorithms have been proposed, they are not suitable for implementation in embedded environments. In this paper proposes a CENTRIST-based pedestrian detection method which combines census transform and histogram, instead of the HOG method, which is more complicated. An Aldebaran board (300MHz) was used for implemntation of the algorithm in embedded enviornments, and the 512x360 pixel input image was used. The implemented algorithm demonstrated performance of 0.7 frame per second. 1 Introduction Pedestrian detection using computer vision is a technology which involves detecting persons standing or walking through images. A large number of researches on this technology have been conducted in many applied fields. Pedestrian detection can be applied to various fields, such as automatic driving, driving support systems, security systems and smart traffic systems. This technology holds great importance as it has the potential of reducing road accident casualties by detecting pedestrians. It is a very tricky task, however, to maintain high detection rate when the system has to find pedestrians in images with a wide variety of postures, lights and backgrounds. This problem mainly stems from lack of visual features which make it possible to distinguish pedestrians from the surrounding environment. To solve this problem, this paper used Sobel, CENTRIST (CENsus Transform histogram) [1] to identify visual features for detecting pedestrians. And it attempted to implement an algorithm capable of detecting pedestrians in embedded environments, and measured its performance. 2 Related Work Many researches have been conducted on pedestrian detection. The Histogram of Oriented Gradient (HOG) [2] method, which is currently the most basic pedestrian ISSN: ASTL Copyright 2014 SERSC

2 detection method, identifies objects by deriving features of gradient distribution within the image. The problem with this method, however, is that it takes longer to process. The Cascade HOG [3] method was developed as a solution to this problem, by applying the cascade technique to the HOG method. The Cascade HOG method detects pedestrians by calculating HOG from blocks with various positions and sizes. While the conventional methods detect pedestrians using features within 1-channel images, the ChnFrts [4] method detects pedestrians using a number of channels. The ChnFrts method identifies features from the image from various perspectives, comprehensively using such information as RGB color values, brightness values, edge values and gradient values. 3 Proposed Algorithm 3.1 Census Transform The Census Transform algorithm [5] is used for stereo matching and deriving feature points. It converts images by comparing strength with the surrounding pixels. The Census Transform algorithm creates a 3x3 census transform window, and arranges the neighboring pixels of the center pixel into a bit string. Formula (1) is a formula for converting the pixels into a bit string. p xy = 0 if p center > p xy 1 else if p center p xy (1) Fig. 1 represents the result of calculating the bit string of a census transform window using the formula ( ) 2 CT= Fig. 1. A Bit String converted from a CT Window 3.2 Histogram Histogram [6] is useful for showing the brightness information of an image. Fig. 2 shows an actual example of a histogram of an image. 124 Copyright 2014 SERSC

Pixel Number Pixel 0 128 255 Value Fig. 2. An Actual Example of Histogram Pixel brightness of 1-channel images, represented on the horizontal axis, ranges from 0 to 255.

3 Pixel Number Pixel Value Fig. 2. An Actual Example of Histogram Pixel brightness of 1-channel images, represented on the horizontal axis, ranges from 0 to 255. The vertical axis represents number of pixels corresponding to each pixel brightness value, and the number of pixels vary depending on the brightness and size of the image. By analyzing the histogram of an image, we can identify the brightness distribution and contrast of the image. This information can be used for image quality improvement and object detection. 4 Implementation Method and Result This paper implemented a CENTRIST-based pedestrian detection algorithm in embedded environments, and an Aldebaran board (300MHz) was used for the experiment. The 512x360 pixel input image was used. As for the dataset for pedestrian detection, the Person Dataset was used to create the training data. In order to implement the pedestrian detection algorithm using an Aldebaran board without floating-point unit(fpu), double type training data were converted to int type training data. The CENTRIST-based pedestrian detection algorithm was implemented in an Aldebaran board, which is an embedded environment, and the implemented algorithm demonstrated performance of 0.7 frame per second. Fig. 3 is the result of pedestrian detection using an Aldebaran board. Fig. 3. Pedestrian Detection using an Aldebaran Board. Table 1 compares pedestrian detection performance of the method proposed in this paper with those of HOG [2] and ChnFtrs [4]. Copyright 2014 SERSC 125

4 Table 1. Comparison of Pedestrian Detection Performance Frame Rate Image Size HOG [2] fps 640x480 ChnFtrs [4] 0.5 fps 640x480 This Paper 0.7 fps 512x360 5 Conclusion This paper implemented a CENTRIST-based pedestrian detection algorithm, which is more suitable for embedded environments compared with conventional pedestrian detection algorithms, using an embedded board. An Aldebaran board (300MHz) was used for implemntation of the algorithm in embedded enviornments, and the 512x360 pixel input image was used. The implemented algorithm demonstrated performance of 0.7 frame per second. While pedestrian detection is mostly implemented on personal computers due to the amount of computing required, implementation of pedestrian detection suitable for embedded environments will contribute to a wide variety of researches applicable to various fields such as smart cars and traffic lights with automated pedestrian detection capability. Acknowledgments. This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the Human Resource Development Project for SoC support program (NIPA-2014-H ) supervised by the NIPA (National IT Industry Promotion Agency). References 1. Wu, J., James M.Rehg: CENTRIST: A visual descriptor for scene categorization. IEEE TPAMI, vol.33, pp (2011) 2. Navneet Dalal, Bill Triggs: Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.1, pp (2005) 3. Zhu, Q., Avidan, S., Yeh, M.C., Cheng, K. T.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. IEEE Computer Society Conference of Computer Vision and Pattern Recognition, vol.2, pp (2006) 4. Dollar, P., Tu, Z., Perona, P., and Belongie, S.: Integral Channel Features. British Machine Vision Conference (2009) 5. Zabih, R. and Woodfill, J.: Non-parametric Local Transforms for Computing Visual Correspondence. In Proceedings of the European Conference on Computer Vision, pp (1994) 126 Copyright 2014 SERSC

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