Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

자율주행을 위한 정지 장애물 맵과 GMFT 융합 기반 이동 물체 탐지 및 추적

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 이경수.Based on the high accuracy of LiDAR sensor, detection and tracking of moving objects(DATMO) have been advanced as an important branch of perception for an autonomous vehicle. However, due to crowded road circumstances by various kind of vehicles and geographical features, it is necessary to reduce clustering fail case and decrease the computational burden. To overcome these difficulties, this paper proposed a novel approach by integrating DATMO and mapping algorithm. Since the DATMO and mapping are specialized to estimate moving object and static map respectively, these two algorithms can improve their estimation by using each others output. Whole perception algorithm is reconstructed using feedback loop structure includes DATMO and mapping algorithm. Moreover, mapping algorithm and DATMO are revised to innovative Bayesian rule-based Static Obstacle Map(SOM) and Geometric Model-Free Tracking(GMFT) to use each others output as the measurements of filtering process. The proposed study is evaluated via driving dataset collected by vehicles with RTK DGPS, RT-range and 2D LiDAR. Several typical clustering fail cases that had been observed in existing DATMO approach are reduced and code operation time over the whole perception process is decreased. Especially, estimation of moving vehicles state include position, velocity, and yaw angle show less error with references which are measured by RT-range.라이다 센서의 측정 정밀성을 기반으로 하여 DATMO, 즉 이동 물체 탐지 및 추적은 자율주행 인지 분야의 매우 중요한 주제로 발전되어 왔다. 그러나 다양한 종류의 차량에 의해 도로 상황이 복잡한 점 및 도로 특유의 복잡한 지형적 특성 때문에 클러스터링(Clustering)의 실패 사례가 종종 발생할 뿐만 아니라 인지 알고리즘의 계산 부담도 증가한다. 이러한 문제를 극복하기 위해 이 논문에서는 DATMO 알고리즘과 맵핑 알고리즘을 통합하여 새로운 접근법을 제시하였다. DATMO와 맵핑 알고리즘은 각각 이동 물체와 정지 물체의 상태를 추정하는데에 특화되어있기 때문에 두 알고리즘은 서로의 출력을 입력으로 사용하여 추정 성능을 향상시킬 수 있다. 전체 인지 알고리즘은 DATMO와 맵핑 알고리즘을 포함하는 피드백 루프 구조로 재구성된다. 또한 두 알고리즘은 각각 Geometric Model-Free Tracking(GMFT)과 베이지안 룰 기반의 혁신적인 Static Obstacle Map(SOM)으로 수정되어 서로의 출력을 필터링 프로세스의 측정값으로 사용한다. 이 연구에서 제시한 통합 인지 알고리즘은 RTK DGPS와 RT Range 장비, 그리고 2차원 LiDAR를 장착한 차량을 이용하여 수집한 데이터를 통해 성능을 평가하였다. 기존의 DATMO 연구에서 발생했던 몇 가지 일반적인 클러스터링 실패 사례가 감소하였고 전체 통합 인지 과정에 대한 알고리즘 작동 시간이 감소함을 확인하였다. 특히, 이동하는 물체의 위치, 속도, 방향을 추정한 결과는 RT Range 장비로 측정한 실제 값과 기존 방식 대비 더욱 적은 오차를 보여주었다.Chapter 1 Introduction 1 Chapter 2 Interaction of Mapping and DATMO 5 Chapter 3 Mapping – Static Obstacle Map 9 3.1 Prediction of SOM 11 3.2 Measurement update of SOM 14 Chapter 4 DATMO – Geometric Model-Free Tracking 16 4.1 Prediction of target state 18 4.2 Track management 19 4.3 Measurement update of target state 21 Chapter 5 Experimental Results 23 5.1 Vehicles and sensors configuration 24 5.2 Detection rate of moving object 27 5.3 State estimation accuracy of moving object 31 5.4 Code operation time 34 Chapter 6 Conclusion and Future Work 36 6.1 Conclusion 36 6.2 Future works 37 Bibliography 39 초 록 43Maste

Robust Motion Segmentation from Pairwise Matches

In this paper we address a classification problem that has not been considered before, namely motion segmentation given pairwise matches only. Our contribution to this unexplored task is a novel formulation of motion segmentation as a two-step process. First, motion segmentation is performed on image pairs independently. Secondly, we combine independent pairwise segmentation results in a robust way into the final globally consistent segmentation. Our approach is inspired by the success of averaging methods. We demonstrate in simulated as well as in real experiments that our method is very effective in reducing the errors in the pairwise motion segmentation and can cope with large number of mismatches

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure