2014 IEEE International Conference on Consumer Electronics (ICCE)
Advanced Driver Assistant System Based on Monocular Camera Jun-Su Kang, Jihun Kim and Minho Lee* School of Electronics Engineering, Kyungpook National University, Taegu, Korea {wkjuns, ceuree}@gmail.com,
[email protected] Abstract—An estimated 20-50 million people get injured and at least 1.2 million people die in automotive accidents every year. To avoid this, several security systems such as lane departure warning system, vehicle detection system, advanced cruise control system, etc., have been developed. But they are very expensive. In our research, we focus on developing low cost intelligent vehicle systems. In this paper, we propose a driver assistant system based on monocular camera. The proposed system consists of two parts. First part is vehicle detection module based on adaptive boosting using extent modified census transform (EMCT). Second part is lane detection module using RANdom Sample Consensus (RANSAC). Experimental results of system shows that both modules perform robustly with less computational load and that it can be a reliable intelligent vehicle system.
II. OVERVIEW OF PROPOSED ADAS A. Vehicle detection module As shown in Fig. 1, the vehicle detection module has three parts: (1) feature extraction using EMCT features, (2) finding a weak classifier and strong classifier, (3) cascaded learning.
I. INTRODUCTION An important challenge of automotive industry is to develop low cost advanced driver assistance systems (ADAS), which can increase traffic safety. Advances in computer vision research has played a vital role in this direction and some ADAS features have been developed relying on camera based systems [1, 2]. For instance, lane departure warning system and lateral control have been developed by detecting the lane markings of the road using forward-facing camera and computer vision techniques. Car manufacturing companies like Toyota, Honda, BMW have also developed several ADAS [3]. However, the problem is that these ADAS features are still very expensive and beyond the reach of normal users. So, considering the emerging need of cheap and affordable ADAS features, we propose an intelligent vehicle system in this paper. Our system is based on monocular camera (car black box). The car black box is popular, cheap and affordable. The proposed system has 2 modules: (1) vehicle detection module and (2) lane detection module. The vehicle detection module use EMCT feature. EMCT feature is extended version of MCT (Modified Census Transform) feature. We use MCT features because of its robustness in illumination change and real world’s lighting conditions rapidly change (especially in driving conditions). Although, MCT has been used in face detection [4, 5] so far, we adapt it for detecting features of driving scenario. For lane detection module, we use RANSAC [6, 7]. We also try to reduce the computation load in our proposed system.
This research was supported by the MSIP(Ministry of Science, ICT & Future Planning), Korea, under the C-ITRC(Convergence Information Technology Research Center) support program (NIPA-2013-H0401-13-1005) supervised by the NIPA(National IT Industry Promotion Agency.)
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Fig 1. The frame work of proposed vehicle detection system (a) The flow chart (b) A cascade process
1) Feature We use Extent Modified Census Transform (EMCT) feature because the MCT feature is already known to be robust in illumination change in environment. In general, MCT features are calculated by 3൞ 3 kernel or square type kernel. Assuming the kernel size is 3, let N’(x) be a local spatial neighborhood of the pixel at x so that ǽ ’(x) = ǽ (x) x. The intensity mean on this neighborhood is denoted by m(x). And, let a comparison function ˣ (I(x), I(x’)) be 1 if I(x)
