Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features

The information acquisition and automatic processing technology based on visual surveillance sensors in intelligent transportation system (ITS) has become an important application field of computer vision technology. The first step of a visual traffic surveillance system usually needs to correctly detect objects from videos and classify them into different categories. In this paper, the improved spatiotemporal sample consistency algorithm (STSC) is proposed, to enhance the robustness of background subtraction in complex scenes. To address this challenge of classifying acquired from visual traffic surveillance sensors in a particular area in China, improved spatiotemporal sample consistency algorithm is proposed, which consists of two main stages. In the first stage, the robustness of moving object detection is further provided by the method we proposed based spatiotemporal sample consistency; in the second stage, we propose the target classification method based prior knowledge, in addition correcting in tracking progress. The experiments on the CDnet 2014, MIO-TCD, and BIT-Vehicle show that the method we proposed successfully overcomes the adverse effects in the complex environment with different shooting angle and resolution taken by single fixed cameras, besides effectively reduces the false alarm rate of classification.