As traditional sliding window detectors need to search the whole image by exhaustive method, a visual attention mechanism-aided target detection model by the particle window is proposed to reduce the calculational load while containing high detection accuracy. This model takes the target saliency as prior information of searching process, and then extracts the region of interest containing true target position by the “Image Signature” saliency map generator and entropy threshold. By uniformly drawing particle windows in an image range corresponding to the saliency targets with Monte Carlo sampling, the local region is treated as candidate detection points, thus resampling is carried out according to corresponding particle windows response. This strategy only focuses on the areas where the objects are potentially present and avoiding the tradeoff between accuracy and efficiency resulting from searching steps. A multi-stage classifier with Adaboost+HLF and SVM+ HOG is established, the former is applied to once-over and the latter is used to locate precisely. The target detection model proposed is compared with the traditional sliding window method and particle window method, and the results show that the Receiver Operating Characteristic(ROC) curve by proposed method contains the area to be larger than that of the other methods and the time consuming is only 1/3 to 1/4 that of the sliding window method and 1/2 that of the particle window method. It increases significantly detection speeds at maintaining high precision detection speed and achieves fast and accurate target detection.