Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

Toward human-centered automated driving: a novel spatial-temporal vision transformer-enabled head tracker

Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, this article proposes a low-cost and markerless headtracking system using a deep learning-based dynamic head pose estimation model. The proposed system requires only a red, green, blue (RGB) camera without other hardware or markers. To enhance the accuracy of the driver’s head pose estimation, a spatiotemporal vision transformer (ST-ViT) model, which takes an image pair as the input instead of a single frame, is proposed. Compared to a standard transformer, the ST-ViT contains a spatial–convolutional vision transformer and a temporal transformer, which can improve the model performance. To handle the error fluctuation of the head pose estimation model, this article proposes an adaptive Kalman filter (AKF). By analyzing the error distribution of the estimation model and the user experience of the head tracker, the proposed AKF includes an adaptive observation noise coefficient; this can adaptively moderate the smoothness of the curve. Comprehensive experiments show that the proposed system is feasible and effective, and it achieves a state-of-the-art performance.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityThis work was supported in part by in part by the A*STAR National Robotics Program under grant W1925d0046, the Start-Up Grant, Nanyang Assistant Professorship under grant M4082268.050, Nanyang Technological University, Singapore, and the State Key Laboratory of Automotive Safety and Energy under project KF2021

Pixel-Level Deep Multi-Dimensional Embeddings for Homogeneous Multiple Object Tracking

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, explicitly-defined post-processing steps. This work introduces two detection methods that incorporate multi-dimensional embeddings. We train deep CNNs to produce easily-clusterable embeddings for semantic instance segmentation and to enable object detection through pose estimation. The use of embeddings allows the method to identify per-pixel instance membership for both tasks. Our method specifically targets applications that require long-term tracking of homogeneous targets using a stationary camera. Furthermore, this method was developed and evaluated on a livestock tracking application which presents exceptional challenges that generalized tracking methods are not equipped to solve. This is largely because contemporary datasets for multiple object tracking lack properties that are specific to livestock environments. These include a high degree of visual similarity between targets, complex physical interactions, long-term inter-object occlusions, and a fixed-cardinality set of targets. For the reasons stated above, our method is developed and tested with the livestock application in mind and, specifically, group-housed pigs are evaluated in this work. Our method reliably detects pigs in a group housed environment based on the publicly available dataset with 99% precision and 95% using pose estimation and achieves 80% accuracy when using semantic instance segmentation at 50% IoU threshold. Results demonstrate our method\u27s ability to achieve consistent identification and tracking of group-housed livestock, even in cases where the targets are occluded and despite the fact that they lack uniquely identifying features. The pixel-level embeddings used by the proposed method are thoroughly evaluated in order to demonstrate their properties and behaviors when applied to real data. Adivser: Lance C. Pére

구조 감응형 데이터 증강 기법과 혼합 밀도 신경망을 이용한 라이다 기반 3차원 객체 검출 개선

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부, 2023. 2. 곽노준.자율주행자동차, 로봇의 인식 장비로 많이 활용되고있는 라이다 (LiDAR) 는 레이저 펄스를 방출하여 되돌아오는 시간을 계산하여 포인트 클라우드 (point cloud) 형태로 주변 환경을 감지한다. 주변 환경을 감지할때 가장 중 요한 부분은 근처에 어떤 객체가 있는지, 어디에 위치해 있는지를 인식하는 것이고 이러한 작업을 수행하기 위해 포인트 클라우드를 활용하는 3차원 객 체 검출 기술들이 많이 연구되고 있다. 포인트 클라우드 데이터의 전처리 방법에 따라 매우 다양한 구조의 백본 네트워크 (backbone network) 가 연구되고 있다. 고도화된 백본 네트워크들로 인해 인식 성능에 큰 발전을 이루었지만, 이들의 형태가 크게 다르기 때문에 서로 호환성이 부족하여 연구들의 갈래가 많이 나누어지고 있다. 본 논문에 서 풀고자하는 문제는 파편화된 백본 네트워크의 구조들에 구애받지 않고 3차원 객체 검출기의 성능을 향상시킬 방법이 있는가 이다. 이를 위해 본 논문 에서는 포인트 클라우드 데이터 기반의 3차원 객체 검출 기술을 향상시키는 두 가지 방법을 제안한다. 첫 번째는 3차원 경계 상자 (3D bounding box) 의 구조적인 정보의 활용을 최대화하는 구조 감응형 데이터 증강 (PA-AUG) 기법이다. 3차원 경계 상자 라벨은 객체에 딱 맞게 생성되고 방향값을 포함하기 때문에 상자 내에 객체의 구조 정보를 포함하고 있다. 이를 활용하기 위해 우리는 3차원 경계 상자를 구조 감응형 파티션으로 구분하는 방식을 제안하고, 파티션 수준에서 수행되는 새로운 방식의 데이터 증강 기법을 제안한다. PA-AUG는 다양한 형태의 3차원 객체 검출기들의 성능을 강인하게 만들어주고, 학습 데이터를 2.5배 증 강시키는 만큼의 인식 성능 향상 효과를 보여준다. 두 번째는 혼합 밀도 신경망 기반 3차원 객체 검출 (MD3D) 기법이다. MD3D는 가우시간 혼합 모델 (Gaussian Mixture Model) 을 이용해 3차원 경 계 상자 회귀 문제를 밀도 예측 방식으로 재정의한 기법이다. 이러한 방식은 기존의 라벨 할당식의 학습 방법들과 달리 포인트 클라우드 전처리 형태에 구애받지 않고 동일한 학습 방식을 적용할 수 있다. 또한 기존 방식 대비 학습 에 필요한 하이퍼 파라미터가 현저히 적어서 최적화가 용이하여 인식 성능을 크게 높일 수 있을 뿐만 아니라 간단한 구조로 인해 인식 속도도 빨라지게 된다. PA-AUG와 MD3D는 모두 백본 네트워크 구조에 상관없이 다양한 3차원 객체 검출기에 공통적으로 사용될 수 있으며 높은 인식 성능 향상을 보여준다. 뿐만 아니라 두 기법은 검출기의 서로 다른 영역에 적용되는 기법이므로 함께 동시에 사용할 수 있고, 함께 사용했을때 인식 성능이 더욱 크게 향상된다.LiDAR (Light Detection And Ranging), which is widely used as a sensing device for autonomous vehicles and robots, emits laser pulses and calculates the return time to sense the surrounding environment in the form of a point cloud. When recognizing the surrounding environment, the most important part is recognizing what objects are nearby and where they are located, and 3D object detection methods using point clouds have been actively studied to perform these tasks. Various backbone networks for point cloud-based 3D object detection have been proposed according to the preprocessing method of point cloud data. Although advanced backbone networks have made great strides in detection performance, they are largely different in structure, so there is a lack of compatibility with each other. The problem to be solved in this dissertation is How to improve the performance of 3D object detectors regardless of their diverse backbone network structures?. This dissertation proposes two general methods to improve point cloud-based 3D object detectors. First, we propose a part-aware data augmentation (PA-AUG) method which maximizes the utilization of structural information of 3D bounding boxes. Since the 3D bounding box labels fit the objects boundaries and include the orientation value, they contain the structural information of the object in the box. To fully utilize the intra-object structural information, we propose a novel partaware partitioning method which separates 3D bounding boxes with characteristic sub-parts. PA-AUG applies newly proposed data augmentation methods at the partition level. It makes various types of 3D object detectors robust and brings the equivalent effect of increasing the train data by about 2.5×. Second, we propose a mixture-density-based 3D object detection (MD3D). MD3D predicts the distribution of 3D bounding boxes using a Gaussian mixture model (GMM). It reformulates the conventional regression methods as a density estimation problem. Thus, unlike conventional target assignment methods, it can be applied to any 3D object detector regardless of the point cloud preprocessing method. In addition, as it requires significantly fewer hyper-parameters compared to existing methods, it is easy to optimize the detection performance. MD3D also increases the detection speed due to its simple structure. Both PA-AUG and MD3D can be applied to any 3D object detector and shows an impressive increase in detection performance. The two proposed methods cover different stages of the object detection pipeline. Thus, they can be used simultaneously, and the experimental results show they have a synergy effect when applied together.1 Introduction 1 1.1 Problem Definition 3 1.2 Challenges 6 1.3 Contributions 8 1.3.1 Part-Aware Data Augmentation (PA-AUG) 8 1.3.2 Mixture-Density-based 3D Object Detection (MD3D) 9 1.3.3 Combination of PA-AUG and MD3D 10 1.4 Outline 10 2 Related Works 11 2.1 Data augmentation for Object Detection 11 2.1.1 2D Data augmentation 11 2.1.2 3D Data augmentation 12 2.2 LiDAR-based 3D Object Detection 13 2.3 Mixture Density Networks in Computer Vision 15 2.4 Datasets 16 2.4.1 KITTI Dataset 16 2.4.2 Waymo Open Dataset 18 2.5 Evaluation metric 19 2.5.1 Average Precision (AP) 19 2.5.2 Average Orientation Similarity (AOS) 22 2.5.3 Average Precision weighted by Heading (APH) 22 3 Part-Aware Data Augmentation (PA-AUG) 24 3.1 Introduction 24 3.2 Methods 27 3.2.1 Part-Aware Partitioning 27 3.2.2 Part-Aware Data Augmentation 28 3.3 Experiments 33 3.3.1 Results on the KITTI Dataset 33 3.3.2 Robustness Test 36 3.3.3 Data Efficiency Test 38 3.3.4 Ablation Study 40 3.4 Discussion 41 3.5 Conclusion 42 4 Mixture-Density-based 3D Object Detection (MD3D) 43 4.1 Introduction 43 4.2 Methods 47 4.2.1 Modeling Point-cloud-based 3D Object Detection with Mixture Density Network 47 4.2.2 Network Architecture 49 4.2.3 Loss function 52 4.3 Experiments 53 4.3.1 Datasets 53 4.3.2 Experiment Settings 53 4.3.3 Results on the KITTI Dataset 54 4.3.4 Latency of Each Module 56 4.3.5 Results on the Waymo Open Dataset 58 4.3.6 Analyzing Recall by object size 59 4.3.7 Ablation Study 60 4.3.8 Discussion 65 4.4 Conclusion 66 5 Combination of PA-AUG and MD3D 71 5.1 Methods 71 5.2 Experiments 72 5.2.1 Settings 72 5.2.2 Results on the KITTI Dataset 73 5.3 Discussion 76 6 Conclusion 77 6.1 Summary 77 6.2 Limitations and Future works 78 6.2.1 Hyper-parameter-free PA-AUG 78 6.2.2 Redefinition of Part-aware Partitioning 79 6.2.3 Application to other tasks 79 Abstract (In Korean) 94 감사의 글 96박

An integrated framework for multi-state driver monitoring using heterogeneous loss and attention-based feature decoupling

Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze estimation, this paper proposes a unified network architecture that tackles these estimations as soft classification tasks. A feature decoupling module was developed to decouple the extracted features from different axis domains. Furthermore, a cascade cross-entropy was designed to restrict large deviations during the training phase, which was combined with the other features to form a heterogeneous loss function. In addition, gaze consistency was used to optimize its estimation, which also informed the model architecture design of the gaze estimation task. Finally, the proposed method was verified on several widely used benchmark datasets. Comprehensive experiments were conducted to evaluate the proposed method and the experimental results showed that the proposed method could achieve a state-of-the-art performance compared to other methods