In this paper, we propose a new algorithm to enhance global and local contrast of infrared image. The aim of our research is to carry out pretreatment for infrared video moving objects tracking. For the inherent difficulty, it is difficult for infrared image enhancement to get comparatively ideal result by adopting only one kind of method. So we propose a novel algorithm. First an enhancement algorithm is proposed based on plateau histogram using a self-adaptive threshold to enhance global contrast, which is different from the traditional histogram equalization algorithm. Second step, after getting a nonlinear gain function, the equalized infrared image is transformed by discrete stationary wavelet. Then the high frequency sub-bands are enhanced with the gain function better than linear filter. It also can suppress the amplification of noise. Experimental results show that the new algorithm can enhance the contrast of infrared image effectively and get more excellent visual effect than traditional histogram equalization method and unsharp masking method.
The pseudo-color processing for infrared (IR) image is useful for object detection and tracking. Image fusion of IR and visual images is an effectual scheme for pseudo-color processing. But in some conditions, we only have the IR images of the scene. How to perform a pseudo-color processing for these IR images is a difficult and interesting problem. In the paper, a novel image fusion method based on wavelet and color transfer is proposed. With wavelet and color transfer, the chromaticity values of the color visual image are assigned to the IR image. By the method, the IR and visual images can be fused even if the scenes of two images are not the same one. The only requirement of the method is that the compositions of the source and target scenes resemble each other. The experimental results show that the algorithm can give IR image a natural color appearance. Such a full color representation of nighttime scenes may be of great ergonomic value by making the interpretation of the displayed scene easier (more intuitive) for the observer.
Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.
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