OccCasNet：面向光场深度估计的遮挡感知级联代价体

Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion

Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial o…

Human pose estimation via Convolutional Part Heatmap Regression

This paper is on human pose estimation using Convolutional Neural Networks. Our main contribution is a CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions. To this end, we propose a detection-followed-by-regression CNN cascade. The first part of our cascade outputs part detection heatmaps and the second part performs regression on these heatmaps. The benefits of the proposed architecture are multi-fold: It guides the network where to focus in the image and effectively encode…

基于位置感知复制-粘贴数据增强的遮挡场景下的实例分割

Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation

本文提出位置感知复制-粘贴数据增强技术，结合HTC框架与SWA策略解决篮球场景实例分割中的遮挡问题，在ACM MMSports 2023挑战赛中以0.533遮挡分数排名第一。

Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered. In addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the pro…

Cascade Graph Neural Networks for RGB-D Salient Object Detection

In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient object detection fromRGB-D images is how to fully leverage the two complementary data sources. Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors.In this work, we introduceCascade Graph Neural Networks(Cas-Gnn…

SMILEtrack：面向遮挡感知多目标跟踪的相似度学习

SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking

本文提出 SMILEtrack，结合相似度学习模块与带 GATE 函数的匹配级联，以改善遮挡场景下的多目标跟踪精度与速度权衡。

Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame po…

每个角度都值得再看一眼：从多视角关节云中挖掘运动学骨骼结构

Every Angle Is Worth A Second Glance: Mining Kinematic Skeletal Structures from Multi-view Joint Cloud

本文针对稀疏多视角多人动作捕捉中的遮挡干扰问题，提出通过构建关节云并利用JCSAT网络中的级联编码器和OTAP模块挖掘运动学骨骼结构，并发布BUMocap-X数据集，在遮挡场景下性能超越现有方法。

Anti-Confusing: Region-Aware Network for Human Pose Estimation

In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation. Specifically, the proposed method addresses three key aspects, i.e., data augmentation, feature learning and prediction fusion, respectively. First, we propose Parsing-based Data Augmentation (PDA) to generate abundant data that synthesizes confusing textures. Second, we not only propose a Feature Pyramid Stem (FPS) to learn stronger low-level features in lower stage; but also inc…

View Invariant Human Body Detection and Pose Estimation from Multiple Depth Sensors

Point cloud based methods have produced promising results in areas such as 3D object detection in autonomous driving. However, most of the recent point cloud work focuses on single depth sensor data, whereas less work has been done on indoor monitoring applications, such as operation room monitoring in hospitals or indoor surveillance. In these scenarios multiple cameras are often used to tackle occlusion problems. We propose an end-to-end multi-person 3D pose estimation network, Point R-CNN, using multiple point cloud sources. We conduct extensive experiments to simulate challenging real worl…