In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation. In addition, these methods mainly focus on 2D object detection and cannot estimate detailed properties of objects. In this paper, we propose subcategory-aware CNNs for object detection. We introduce a novel region proposal network that uses subcategory information to guide the proposal generating process, and a new detection network for joint detection and subcategory classification. By using subcategories related to object pose, we achieve state-of-the-art performance on both detection and pose estimation on commonly used benchmarks.
Subcategory has been widely utilized to facilitate object detection, and different methods of discovering object subcategories have been proposed. In DPM @cite_11 , subcategories are discovered by clustering objects according to the aspect ratio of their bounding boxes. @cite_34 performs clustering according to the viewpoint of the object to discover subcategories. Visual subcategories are constructed by clustering in the appearance space of object @cite_17 @cite_35 @cite_30 @cite_24 . 3DVP @cite_33 performs clustering in the 3D voxel space according to the visibility of the voxels. Unlike previous works, we utilize subcategory to improve CNN-based detection, and our framework is general to employ different types of object subcategories.
@cite_30 @cite_35 @cite_11 @cite_33 @cite_24 @cite_34 @cite_17
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People can immediately and precisely identify that an image contains 1, 2, 3 or 4 items by a simple glance. The phenomenon, known as Subitizing, inspires us to pursue the task of Salient Object Subitizing (SOS), i.e. predicting the existence and the number of salient objects in a scene using holistic cues. To study this problem, we propose a new image dataset annotated using an online crowdsourcing marketplace. We show that a proposed subitizing technique using an end-to-end Convolutional Neural Network (CNN) model achieves significantly better than chance performance in matching human labels on our dataset. It attains 94 accuracy in detecting the existence of salient objects, and 42–82 accuracy (chance is 20 ) in predicting the number of salient objects (1, 2, 3, and 4+), without resorting to any object localization process. Finally, we demonstrate the usefulness of the proposed subitizing technique in two computer vision applications: salient object detection and object proposal. In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (, ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the @math gPb-ucm approach of (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (, TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. In this paper we present a comprehensive object categorization and classification system, of great importance for mobile manipulation applications in indoor environments. In detail, we tackle the problem of recognizing everyday objects that are useful for a personal robotic assistant in fulfilling its tasks, using a hierarchical multi-modal 3D-2D processing and classification system. The acquired 3D data is used to estimate geometric labels (plane, cylinder, edge, rim, sphere) at each voxel cell using the Radius-based Surface Descriptor (RSD). Then, we propose the use of a Global RSD feature (GRSD) to categorize point clusters that are geometrically identical into one of the object categories. Once a geometric category and a 3D position is obtained for each object cluster, we extract the region of interest in the camera image and compute a SURF-based feature vector for it. Thus we obtain the exact object instance and the orientation around the object's up-right axis from the appearance. The resultant system provides a hierarchical categorization of objects into basic classes from their geometry and identifies objects and their poses based on their appearance, with near real-time performance. We validate our approach on an extensive database of objects that we acquired using real sensing devices, and on both unseen views and unseen objects. Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark [17] and the outdoor-scene dataset [41]. We improve state-of-the-art results on car detection and pose estimation with notable margins (6 in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.