Abstract. In this work, to effectively detect moving objects in a fixed camera scene, we propose a novel background subtraction framework employing diverse cues: pixel texture, pixel color and region appearance. The texture information of the scene is clustered by the conventional codebook based background modeling technique, and utilized to detect initial foreground regions. In this process, we employ a new texture operator namely, scene adaptive local binary pattern (SALBP) that provides more consistent and accurate texture-code generation by applying scene adaptive multiple thresholds. Background statistics of the color cues are also modeled by the codebook scheme and employed to refine the texturebased detection results by integrating color and texture characteristics. Finally, appearance of each refined foreground blob is verified by measuring the partial directed Hausdorff distance between the shape of a blob boundary and the edge-map of the corresponding sub-image region in the input frame. The proposed method is compared with other state-of-theart background subtraction techniques and its results demonstrate that our method outperforms others for complicated environments in video surveillance applications.

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SUMMARYAs the number of surveillance cameras keeps increasing, the demand for automated traffic-monitoring systems is growing. In this paper, we propose a practical vehicle detection method for such systems. In the last decade, vehicle detection mainly has been performed by employing an image scan strategy based on sliding windows whereby a pre-trained appearance model is applied to all image areas. In this approach, because the appearance models are built from vehicle sample images, the normalization of the scales and aspect ratios of samples can significantly influence the performance of vehicle detection. Thus, to successfully apply sliding window schemes to detection, it is crucial to select the normalization sizes very carefully in a wise manner. To address this, we present a novel vehicle detection technique. In contrast to conventional methods that determine the normalization sizes without considering given scene conditions, our technique first learns local region-specific size models based on scenecontextual clues, and then utilizes the obtained size models to normalize samples to construct more elaborate appearance models, namely local sizespecific classifiers (LSCs). LSCs can provide advantages in terms of both accuracy and operational speed because they ignore unnecessary information on vehicles that are observable in faraway areas from each sliding window position. We conduct experiments on real highway traffic videos, and demonstrate that the proposed method achieves a 16% increased detection accuracy with at least 3 times faster operational speed compared with the state-of-the-art technique.