Abstract: Exhaustive search method of sliding window which consumes much time in searching the location is used to detect objects. In order to solve this problem, we propose a fast object detection algorithm with non-sliding window based on accumulation of multi-scale local response. Firstly, the multi-scale and foldable local areas are extracted as the training sample, then learning them to obtain a part sets with multi-scale and discriminative ability, in which every local area and object have a definely position corresponding relationship; Secondly, the appearance of a particular area is based on every projection detector’s response and the object’s positions are determined by using voting shceme with multi-scale object detection results of the local area and the position constraint; Finally, we test the object by combining the HOG feature extraction with the classifier of SVM to realize accurate location. Experimental results show that the proposed method which combining multi-scale part model and statistics of cumulative voting and the classifier of SVM improves the detection efficiency via saving the consumed time of the sliding window pixel-by-pixel searches background.