maps the image to a density map, such that the integral over any sub region gives the count of objects within that region [@LempitskyZ10; @ArtetaLNZ14; @RodriguezLSA11; @ZhangLWY15]. In [@LempitskyZ10], the pixel-level density is learned by minimizing a regularized risk quadratic cost function. Based on the density map, an interactive counting system is introduced in [@ArtetaLNZ14] to incorporate the relevance feedback. Instead of hand-crafted feature, [@ZhangLWY15] focuses on the CNN-based density map and instance count estimation in cross-scene scenario. While the density map provides certain clue of the crowdedness, it still lacks the exact position of each instance.

### Detection-based Approach

gives the total count by localizing each object instance. Such approaches can be considered as the application of object detection and thus benefit a lot from the improvement of object detection [@Girshick15; @RenHGS15; @LiuAESRFB16; @RedmonF17]. However, directly applying object detection for object counting requires special focus on small-scale objects and extraordinary effort on massive instance labeling. In [@LinD10], a hierarchical part-template matching approach is proposed to detection humans, which requires careful feature and template design, while in [@HsiehLH17], the neural network learning is applied to detect and count car instances by incorporating layout information. With its large potential in applications 