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In this paper, we propose a novel over-segmentation based method for the detection of foreground objects from a surveillance video by integrating techniques of background modeling and Markov Random Fields classification. Firstly, we introduce a fast affinity propagation clustering algorithm to produce the over-segmentation of a reference image by taking(More)
As the products based on Android platform have been widely spread in consumer electronics market, the needs for systematic performance analysis have significantly increased. Conventional approaches rely on publicly open performance analysis tools in Android SDK or Linux community such as DDMS (Dalvik Debug Monitor Server), LTTng, Oprofile, and Ftrace.(More)
A novel multi-attribute sparse representation enforced with group constraints is proposed in this paper. Data with multiple attributes can be represented by individual binary matrices to indicate the group properties for each data sample. Then, these attribute matrices are incorporated into the formulation of l1-minimization. The solution is obtained by(More)
Background subtraction is commonly used to detect foreground objects in video surveillance. Traditional background subtraction methods are usually based on the assumption that the background is stationary. However, they are not applicable to dynamic background, whose background images change over time. In this paper, we propose an adaptive Local-Patch(More)
In this paper, we propose an automatic human segmentation algorithm for video conferencing applications. Since humans are the principal subject in these videos, the proposed framework is based on human shape clues to separate humans from complex background and replace or blur the background for immersive communication. We first detect face position and(More)
We present a multi-attributed dictionary learning algorithm for sparse coding. Considering training samples with multiple attributes, a new distance matrix is proposed by jointly incorporating data and attribute similarities. Then, an objective function is presented to learn category-dependent dictionaries that are compact (closeness of dictionary atoms(More)
Vehicle detection methods are playing an important role for driver assistance systems. Developing a high accuracy and efficiency vehicle detection system thus becomes crucial. One of the popular approaches is the scanning method which is based on the sliding window search for locating the vehicles from the input images. Such method provides a high detection(More)
Vehicle detection is an important research problem for Advanced Driver Assistance Systems to improve driving safety. Most existing methods are based on the sliding window search framework to locate vehicles in an image. However, such methods usually produce large numbers of false positives and are computationally intensive. In this paper, we propose an(More)