Towards Accurate Multi-person Pose Estimation in the Wild

We propose a method for multi-person detection and 2-D pose estimation that achieves state-of-art results on the challenging COCO keypoints task. It is a simple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict the location and scale of boxes which are likely to contain people; for this we use the Faster RCNN detector. In the second stage, we estimate the keypoints of the person potentially contained in each proposed bounding box. For each keypoint type we predict dense heatmaps and offsets using a fully convolutional ResNet. To combine these outputs we introduce a novel aggregation procedure to obtain highly localized keypoint predictions. We also use a novel form of keypoint-based Non-Maximum-Suppression (NMS), instead of the cruder box-level NMS, and a novel form of keypoint-based confidence score estimation, instead of box-level scoring. Trained on COCO data alone, our final system achieves average precision of 0.649 on the COCO test-dev set and the 0.643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art. Further, by using additional in-house labeled data we obtain an even higher average precision of 0.685 on the test-dev set and 0.673 on the test-standard set, more than 5% absolute improvement compared to the previous best performing method on the same dataset.Comment: Paper describing an improved version of the G-RMI entry to the 2016 COCO keypoints challenge (http://image-net.org/challenges/ilsvrc+coco2016). Camera ready version to appear in the Proceedings of CVPR 201

Visual object tracking performance measures revisited

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects. In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology

Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation

We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.Comment: CVPR 201

Joint Multi-Person Pose Estimation and Semantic Part Segmentation

Human pose estimation and semantic part segmentation are two complementary tasks in computer vision. In this paper, we propose to solve the two tasks jointly for natural multi-person images, in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations. Specifically, we first train two fully convolutional neural networks (FCNs), namely Pose FCN and Part FCN, to provide initial estimation of pose joint potential and semantic part potential. Then, to refine pose joint location, the two types of potentials are fused with a fully-connected conditional random field (FCRF), where a novel segment-joint smoothness term is used to encourage semantic and spatial consistency between parts and joints. To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments. Finally, to reduce the complexity of the FCRF, we induce human detection boxes and infer the graph inside each box, making the inference forty times faster. Since there's no dataset that contains both part segments and pose labels, we extend the PASCAL VOC part dataset with human pose joints and perform extensive experiments to compare our method against several most recent strategies. We show that on this dataset our algorithm surpasses competing methods by a large margin in both tasks.Comment: This paper has been accepted by CVPR 201

Fine-grained sketch-based image retrieval by matching deformable part models

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. An important characteristic of sketches, compared with text, rests with their ability to intrinsically capture object appearance and structure. Nonetheless, akin to traditional text-based image retrieval, conventional sketch-based image retrieval (SBIR) principally focuses on retrieving images of the same category, neglecting the fine-grained characteristics of sketches. In this paper, we advocate the expressiveness of sketches and examine their efficacy under a novel fine-grained SBIR framework. In particular, we study how sketches enable fine-grained retrieval within object categories. Key to this problem is introducing a mid-level sketch representation that not only captures object pose, but also possesses the ability to traverse sketch and image domains. Specifically, we learn deformable part-based model (DPM) as a mid-level representation to discover and encode the various poses in sketch and image domains independently, after which graph matching is performed on DPMs to establish pose correspondences across the two domains. We further propose an SBIR dataset that covers the unique aspects of fine-grained SBIR. Through in-depth experiments, we demonstrate the superior performance of our SBIR framework, and showcase its unique ability in fine-grained retrieval