The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as cascade), the scanning speed also depends on number of different factors (such as grid spacing, and scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper we present a technique to reduce the number of miss detections while increasing the grid spacing when using the sliding window approach for object detection. This is achieved by using a small patch to predict the bounding box of an object within a local search area. To achieve speed it is necessary that the bounding box prediction is comparable or better than the time it takes in average for the object classifier to reject a subwindow. We use simple features and a decision tree as it proved to be efficient for our application. We analyze the effect of patch size on bounding box estimation and also evaluate our approach on benchmark face database. Since perturbing the training data can have an affect on the final performance, we evaluate our approach for classifiers trained with and without perturbations and also compare with OpenCV. Experimental evaluation shows better detection rate and speed with our proposed approach for larger grid spacing when compared to standard scanning technique.