DeepSignals: Predicting Intent of Drivers Through Visual Signals

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and Automation (ICRA), 201

LSTA: Long Short-Term Attention for Egocentric Action Recognition

Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state of the art performance on four standard benchmarks.Comment: Accepted to CVPR 201

LSTA: Long Short-Term Attention for Egocentric Action Recognition

Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state-of-the-art performance on four standard benchmarks

Deep Learning Techniques for Video Instance Segmentation: A Survey

Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simultaneously. By tackling the video instance segmentation tasks through effective analysis and utilization of visual information in videos, a range of computer vision-enabled applications (e.g., human action recognition, medical image processing, autonomous vehicle navigation, surveillance, etc) can be implemented. As deep-learning techniques take a dominant role in various computer vision areas, a plethora of deep-learning-based video instance segmentation schemes have been proposed. This survey offers a multifaceted view of deep-learning schemes for video instance segmentation, covering various architectural paradigms, along with comparisons of functional performance, model complexity, and computational overheads. In addition to the common architectural designs, auxiliary techniques for improving the performance of deep-learning models for video instance segmentation are compiled and discussed. Finally, we discuss a range of major challenges and directions for further investigations to help advance this promising research field

A Deep Learning Approach to Object Affordance Segmentation

Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the so-called "object affordances". However, most works treat it as a static semantic segmentation problem, focusing solely on object appearance and relying on strong supervision and object detection. In this paper, we propose a novel approach that exploits the spatio-temporal nature of human-object interaction for affordance segmentation. In particular, we design an autoencoder that is trained using ground-truth labels of only the last frame of the sequence, and is able to infer pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism that enables the implicit localization of the interaction hotspot. For evaluation purposes, we introduce the SOR3D-AFF corpus, which consists of human-object interaction sequences and supports 9 types of affordances in terms of pixel-wise annotation, covering typical manipulations of tool-like objects. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF, while being able to predict affordances for similar unseen objects in two affordance image-only datasets.Comment: 5 pages, 4 figures, ICASSP 202

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

3D 손 포즈 인식을 위한 인조 데이터의 이용

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 양한열.3D hand pose estimation (HPE) based on RGB images has been studied for a long time. Relevant methods have focused mainly on optimization of neural framework for graphically connected finger joints. Training RGB-based HPE models has not been easy to train because of the scarcity on RGB hand pose datasets; unlike human body pose datasets, the finger joints that span hand postures are structured delicately and exquisitely. Such structure makes accurately annotating each joint with unique 3D world coordinates difficult, which is why many conventional methods rely on synthetic data samples to cover large variations of hand postures. Synthetic dataset consists of very precise annotations of ground truths, and further allows control over the variety of data samples, yielding a learning model to be trained with a large pose space. Most of the studies, however, have performed frame-by-frame estimation based on independent static images. Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human hand pose estimation is a particularly interesting example of this synthetic-to-real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this dissertation, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows. With the generated dataset, we train a newly proposed recurrent framework, exploiting visuo-temporal features from sequential images of synthetic hands in motion and emphasizing temporal smoothness of estimations with a temporal consistency constraint. Our novel training strategy of detaching the recurrent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust estimations. We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation benchmarks. Since a fixed set of dataset provides a finite distribution of data samples, the generalization of a learning pose estimation network is limited in terms of pose, RGB and viewpoint spaces. We further propose to augment the data automatically such that the augmented pose sampling is performed in favor of training pose estimators generalization performance. Such auto-augmentation of poses is performed within a learning feature space in order to avoid computational burden of generating synthetic sample for every iteration of updates. The proposed effort can be considered as generating and utilizing synthetic samples for network training in the feature space. This allows training efficiency by requiring less number of real data samples, enhanced generalization power over multiple dataset domains and estimation performance caused by efficient augmentation.2D 이미지에서 사람의 손 모양과 포즈를 인식하고 구현흐는 연구는 각 손가락 조인트들의 3D 위치를 검출하는 것을 목표로한다. 손 포즈는 손가락 조인트들로 구성되어 있고 손목 관절부터 MCP, PIP, DIP 조인트들로 사람 손을 구성하는 신체적 요소들을 의미한다. 손 포즈 정보는 다양한 분야에서 활용될수 있고 손 제스쳐 감지 연구 분야에서 손 포즈 정보가 매우 훌륭한 입력 특징 값으로 사용된다. 사람의 손 포즈 검출 연구를 실제 시스템에 적용하기 위해서는 높은 정확도, 실시간성, 다양한 기기에 사용 가능하도록 가벼운 모델이 필요하고, 이것을 가능케 하기 위해서 학습한 인공신경망 모델을 학습하는데에는 많은 데이터가 필요로 한다. 하지만 사람 손 포즈를 측정하는 기계들이 꽤 불안정하고, 이 기계들을 장착하고 있는 이미지는 사람 손 피부 색과는 많이 달라 학습에 사용하기가 적절하지 않다. 그러기 때문에 본 논문에서는 이러한 문제를 해결하기 위해 인공적으로 만들어낸 데이터를 재가공 및 증량하여 학습에 사용하고, 그것을 통해 더 좋은 학습성과를 이루려고 한다. 인공적으로 만들어낸 사람 손 이미지 데이터들은 실제 사람 손 피부색과는 비슷할지언정 디테일한 텍스쳐가 많이 달라, 실제로 인공 데이터를 학습한 모델은 실제 손 데이터에서 성능이 현저히 많이 떨어진다. 이 두 데이타의 도메인을 줄이기 위해서 첫번째로는 사람손의 구조를 먼저 학습 시키기위해, 손 모션을 재가공하여 그 움직임 구조를 학스한 시간적 정보를 뺀 나머지만 실제 손 이미지 데이터에 학습하였고 크게 효과를 내었다. 이때 실제 사람 손모션을 모방하는 방법론을 제시하였다. 두번째로는 두 도메인이 다른 데이터를 네트워크 피쳐 공간에서 align시켰다. 그뿐만아니라 인공 포즈를 특정 데이터들로 augment하지 않고 네트워크가 많이 보지 못한 포즈가 만들어지도록 하나의 확률 모델로서 설정하여 그것에서 샘플링하는 구조를 제안하였다. 본 논문에서는 인공 데이터를 더 효과적으로 사용하여 annotation이 어려운 실제 데이터를 더 모으는 수고스러움 없이 인공 데이터들을 더 효과적으로 만들어 내는 것 뿐만 아니라, 더 안전하고 지역적 특징과 시간적 특징을 활용해서 포즈의 성능을 개선하는 방법들을 제안했다. 또한, 네트워크가 스스로 필요한 데이터를 찾아서 학습할수 있는 자동 데이터 증량 방법론도 함께 제안하였다. 이렇게 제안된 방법을 결합해서 더 나은 손 포즈의 성능을 향상 할 수 있다.1. Introduction 1 2. Related Works 14 3. Preliminaries: 3D Hand Mesh Model 27 4. SeqHAND: RGB-sequence-based 3D Hand Pose and Shape Estimation 31 5. Hand Pose Auto-Augment 66 6. Conclusion 85 Abstract (Korea) 101 감사의 글 103박

STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition

We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github.com/zgzxy001/STMT.Comment: CVPR 202

Deep Learning for Video Object Segmentation:A Review

As one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural networks have shown outstanding performance improvements in many computer vision applications, with video object segmentation being one of the most advocated and intensively investigated. In this paper, we present a systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches. Concretely, we start by introducing the definition, background concepts and basic ideas of algorithms in this field. Subsequently, we summarise the datasets for training and testing a video object segmentation algorithm, as well as common challenges and evaluation metrics. Next, previous works are grouped and reviewed based on how they extract and use spatial and temporal features, where their architectures, contributions and the differences among each other are elaborated. At last, the quantitative and qualitative results of several representative methods on a dataset with many remaining challenges are provided and analysed, followed by further discussions on future research directions. This article is expected to serve as a tutorial and source of reference for learners intended to quickly grasp the current progress in this research area and practitioners interested in applying the video object segmentation methods to their problems. A public website is built to collect and track the related works in this field: https://github.com/gaomingqi/VOS-Review