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Smart aggregation advertisement platform and advertisement display method

The invention relates to a smart aggregation advertisement platform, which comprises two parts: a client and a server. The client is formed by a configuration management module, an advertisement platform priority list distribution module, each advertisement platform advertisement management module, an advertisement platform management module and a log module. The core function of the client is a concurrent request function; and an advertisement request is sent to a plurality of advertisement platforms simultaneously to fill the advertisement, and higher display success rate of advertisement display is ensured. The server part is mainly formed by a configuration service, a log service and an intelligent analysis service. The core function of the server is an intelligent configuration function. An intelligent resource transferring mode is provided; success filling rate of each advertisement platform is calculated in advance and the success rate is updated dynamically; then, the advertisement request filling weight value of each advertisement platform is configured automatically according to the summarized filling rate; and higher weight value is configured to the advertisement platform SDK, the filling rate of which is higher, so that more high-quality advertisements are allowed to be displayed successfully.
Owner:上海拓畅信息技术有限公司

Structure sparse tracking method based on significance weighting

A structure sparse tracking method based on significance weighting comprises: 1, performing modeling of object appearance, inputting the initial state of an object to obtain an object template and a background template, learning a structure sparse dictionary, performing clustering of local images in an object region, constructing a weight dictionary, and calculating the significance weighting vector, the shielding state vector and the significance weighting structure sparse model of the local image; 2, tracking the object, performing sampling of the object state, obtaining the candidate state of a current frame and a corresponding sampling particle, employing affine transformation to map a candidate particle region into a fixed rectangle, calculating a sample model through the structure sparse dictionary and the weighting vector of the local image, calculating the similarity of the object model and the sample module, estimating the current state of the object according to the maximum posterior probability, and at a temperate update phase, and performing online updating of the object template through a template updating strategy with a shielding detection mechanism to avoid tracking drift so as to better adapt the changing of the object appearance.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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