The ultimate purpose of a pedestrian-detectionsystem (PDS) is to reduce pedestrian-vehicle-related injury. Mostsuch systems tend to adopt expensive sensors, such as infrareddevices, in expectation of better performance. In comparison, alow-cost optical-camera-based system has much potential practicalvalue, including a greater detection range, and can easily betrained to detect other objects. However, such low-cost systems aredifficult to design (e.g., little original information can be collected,and the scene is very complex). To address these problems, aneffective and reliable classifier is needed. The classifier should havea proper structure, its features need to be well selected, and a largenumber of high-quality samples are necessary for training. In thispaper, we present a low-cost PDS which only uses a single opticalcamera. We design a cascade classifier to achieve an effective andreliable detection. First, our system scans two sequential framesat each zoom scale with a sliding window. Second, with eachwindow, both appearance and motion features are extracted. Awell-trained cascade classifier, combining statistical learning witha decomposed support-vector-machine classifier, then determineswhether 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 coevolutionaryalgorithm is adopted to identify each pedestrian’s direction, andthe distance of each from the vehicle is also provided using anestimation algorithm. During the training procedure, we select keyfeatures by using the AdaBoost algorithm and a large numberof high-quality samples. Experimental results demonstrate thatthe system is suitable for pedestrian detection in city traffic: Thedetection speed is more than 10 ft/s, the detection rate reaches80%, and the false positive rate is no more than 0.3‰.