A Low-Cost Pedestrian-Detection System With a Single Optical Camera

The ultimate purpose of a pedestrian-detection system (PDS) is to reduce pedestrian-vehicle-related injury. Most such systems tend to adopt expensive sensors, such as infrared devices, in expectation of better performance. In comparison, a low-cost optical-camera-based system has much potential practical value, including a greater detection range, and can easily be trained to detect other objects. However, such low-cost systems are difficult to design (e.g., little original information can be collected, and the scene is very complex). To address these problems, an effective and reliable classifier is needed. The classifier should have a proper structure, its features need to be well selected, and a large number of high-quality samples are necessary for training. In this paper, we present a low-cost PDS which only uses a single optical camera. We design a cascade classifier to achieve an effective and reliable detection. First, our system scans two sequential frames at each zoom scale with a sliding window. Second, with each window, both appearance and motion features are extracted. A well-trained cascade classifier, combining statistical learning with a decomposed support-vector-machine classifier, then determines whether the window contains a human body. At the same time, to provide as much information as possible about the pedestrian, a small-scale weighted template tree trained by a coevolutionary algorithm is adopted to identify each pedestrian's direction, and the distance of each from the vehicle is also provided using an estimation algorithm. During the training procedure, we select key features by using the AdaBoost algorithm and a large number of high-quality samples. Experimental results demonstrate that the system is suitable for pedestrian detection in city traffic: The detection speed is more than 10 ft/s, the detection rate reaches 80%, and the false positive rate is no more than 0.3⁰/₀₀.

I. Introduction

This paper addresses the challenge of how both to guarantee road safety and to reduce pedestrian-vehicle-related injury. A pedestrian-detection system (PDS) is one approach to this challenge. Most PDSs tend to be based on complicated expensive devices such as an infrared camera [1]–[6] or even radar [7] in order to sense as much information as possible to enable detection. For example, Xu et al. [1] proposed a typical PDS with an infrared camera to detect pedestrians in a night scene. Bertozzi et al. [2], [3] proposed a system equipped with a pair of infrared cameras which can detect pedestrians in stereo images. The PROTECTOR system (currently SAVE-U) is arguably the most complicated and practicable PDS so far [7]. Not only can it detect pedestrians in real time, but it also traces them and makes risk assessment. However, its performance appears, to a certain extent, to be a consequence of its high-cost hardware (an optical camera, an infrared camera, and five custom-built radars).