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. INDEX TERMS Moving object detection, vehicle type classification, spatio-temporal, monitoring video.

In order to cope with the shortage of present image encryption algorithm, a novel image encryption algorithm based on chaotic system and fractional Fourier transform is proposed in this paper. The image encryption process includes two steps: first the image is encrypted by employing Fractional Fourier domain double random phase, then the confusion image is encrypted by using confusion matrix which is generated by chaotic system, and finally the cipher image is obtained. The security of the proposed algorithm depends on the sensitivity to the randomness of phase mask, the orders of FRFT and the initial conditions of chaotic system. Theoretical analysis and experimental results demonstrate that the algorithm is favorable.

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