However, to meet the requirements of the object rotation invariance, the process of building the oriented rectangle template rotations is relatively slow for high memory requiring and computational complexity especially in cluttered scenes where large number of feature points need to be described. Most methods are based on template matching, where objects are trained with various indifferent methods and windowed through the scene to find the best matched location. Template matching is attractive for object detection because it can work well for objects with few discriminating features which are dominantly determined based on their overall shape. An early approach to template matching [28] and its extension [29] include the use of the Chamfer distance between the template and the input image contours as a dissimilarity measure. The distance measurement is efficiently computed by distance transformation, but this method is very sensitive to noise and light changes. Another method of distance measurement for binary edge images is Hausdorff distance [30], which measures the maximum of all distances from each edge point in the image to its nearest neighbor in the template. However, it is vulnerable to an occlusion and complex background. Considering the disadvantages of these methods, [PERSON] proposed a novel shape matching algorithm, which can explicitly obtain contour connectivity constraints by constructing a network on the image gradient (Gradient Networks) [31]. While the method can improve the detection accuracy by iteration to calculate the probability of matching each pixel to the object shape, it cannot process video or image sequences in real-time. Generally, a typical template-based matching algorithm considers the template in their entirety but tends to suffer performance complexity issues since the matching process may require searching through a large amount of pixels to find potential matches for the template. Consequently, many template-based algorithms have troubled in real-time object detection, where massive amounts of templates are often required to compensate for the lack of visual properties such as rotation and scale changes. To overcome the limitation, image gradients instead of image contours was proposed for use as matching features [32; 33]. Based on image gradients, Dominant Orientation Templates (DOT) was designed to detect texture-less objects. At the core of DOT is a binary representation of the image and template, choosing a dominant gradient orientation for each region, which can be built efficiently and used to parse images to quickly find objects. Subsequently, based on the work of DOT [32], LINEearizing the memory (LINE2D) [33] was proposed, which is faster and more robust by cache-friendly response maps and spreading the orientations [34]. However, the DOT utilizes only the dominant gradient orientations as the matching feature and cannot adequately describe a texture-less object, resulting in the massive loss of information of the texture-less object and severe degradation of performance or even failure in the presence of occlusion and clutter. By experiments, it is found that the recognition rates of DOT and LINE2D are much inferior to descriptor-based methods. In this paper, we propose a method having general purpose, fast, and high object detection rates by using binarized orientation compressing map and discriminative regional weight. The proposed fast algorithm, which consists of two stages, greatly reduces the computational complexity and false recognition rate. Different from DOT which only exploits the dominant orientations, the proposed method quantizes and encodes all the orientation in a neighborhood to retain more information. In order to improve the computational efficiency, the proposed method compresses the quantized gradient orientation by using a sliding circular window. After the quantization compression process, a set of possible object locations is generated in an orientation similarity measure process.