Multiple Object Detection by a Deformable Part-Based Model and an R-CNN

Multiple object detection is a key challenge in object detection. Feature extraction and occlusion handling are two key elements in multiple object detection. However, existing methods do not perform well in these aspects during detecting multiple objects. A region-based convolutional network (R-CNN) has achieved a great success in region-based feature extraction, and the part filters in a deformable part-based model (DPM) are very suitable for detecting occluded objects. In this letter, we present a framework that integrates the R-CNN and the DPM for detecting multiple objects. In addition, we propose a new filter based on the dense subgraph discovery algorithm for refining the candidate proposals generated by the DPM. Through combining these two models, we can detect each single object with high accuracy among all objects in an image, especially in the situation that objects are stayed close together. Different from the traditional methods, our framework has the capability to detect multiple objects belonging to various classes rather than only one typical class such as a person or a car. Experimental results on the PASCAL VOC dataset show that our framework achieves better performance compared to using only the R-CNN or the DPM alone in multiple object detection.