Object Tracking with a Fusion of Event-Based Camera and Frame-Based Camera

Event cameras are promising sensors that show many advantages over frame-based cameras. Unlike conventional cameras, whose pixels share a common exposure time, event-based cameras represent a novel bio-inspired technology capable of capturing scenes with a high dynamic range and without motion blur. Due to their working principle, an event is generated when a pixel's brightness changes. Therefore, no event data is generated in a scenario where there is no relative motion between the event camera and the scene. However, in this study, we present a new method to enable event generation with a static event camera on a scene with static objects, aiming to eliminate the requirement of relative motion between event camera and the scene. By projecting custom designed grayscale pattern sequences onto static scenes, we successfully triggered a controlled event generation without requiring camera or object motion. Instead of a direct black-to-white transition, we used a sequence of contrast compatible grayscale projection pattern to regulate event rates and prevent bandwidth overload. To prevent event loss over time, we equalized all timestamps to the first timestamp. Since events in event cameras gradually lose their impact and reset over time, this adjustment prevents the decay of event information and ensures a continuous and stable event generation. Despite the absence of motion, we achieved reasonable results in image quality metrics such as MSE, LPIPS, SSIM. In this way, we aim to expand the usage areas of event cameras and make significant progress in data collection processes, especially for static camera and scenes.

This paper presents an asynchronous event-based scheme for automatic intrusion monitoring using Unmanned Aerial Systems (UAS). Event cameras are neuromorphic sensors that capture the illumination changes in the camera pixels with high temporal resolution and dynamic range. In contrast to conventional frame-based cameras, they are naturally robust against motion blur and lighting conditions, which make them ideal for outdoor aerial robot applications. The presented scheme includes two main perception components. First, an asynchronous event-based processing system efficiently detects intrusions by combining several asynchronous event-based algorithms that exploit the advantages of the sequential nature of the event stream. The second is an off-line training mechanism that adjusts the parameters of the event-based algorithms to a particular surveillance scenario and mission. The proposed perception system was implemented in ROS for on-line execution on board UAS, integrated in an autonomous aerial robot architecture, and extensively validated in challenging scenarios with a wide variety of lighting conditions, including day and night experiments in pitch dark conditions.

Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images. In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams. Our quantitative experiments show that our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality ( ), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos ( frames per second) of high-speed phenomena (e.g., a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. As an additional contribution, we demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data. We release the reconstruction code, a pre-trained model and the datasets to enable further research.

Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. Recent work has shown that a continuous-time representation of the event camera pose can deal with the high temporal resolution and asynchronous nature of this sensor in a principled way. In this paper, we leverage such a continuous-time representation to perform visual-inertial odometry with an event camera. This representation allows direct integration of the asynchronous events with microsecond accuracy and the inertial measurements at high frequency. The event camera trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines. This formulation significantly reduces the number of variables in trajectory estimation problems. We evaluate our method on real data from several scenes and compare the results against ground truth from a motion-capture system. We show that our method provides improved accuracy over the result of a state-of-the-art visual odometry method for event cameras. We also show that both the map orientation and scale can be recovered accurately by fusing events and inertial data. To the best of our knowledge, this is the first work on visual-inertial fusion with event cameras using a continuous-time framework.

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 is), very high dynamic range (140dB vs. 60dB), 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.

Event-based cameras, also known as biologically inspired visual sensors, are capable of capturing real-time scene changes efficiently. Unlike traditional frame-based cameras, event cameras solely report triggered pixel-level brightness changes which are referred to as events. Event-based cameras show many advantages such as high temporal resolution, low latency, and high dynamic range, making them very attractive in robotics and computer vision, especially in challenging scenarios that are too demanding for traditional cameras. In this paper, we provide a comprehensive overview of the emerging field of event-based vision, focusing on the operation principle, sampling mechanisms, and algorithms that take advantage of their superior features. We also delve into the various tasks for which event cameras are utilized, such as object tracking, optical flow estimation, 3D reconstruction, SLAM, image reconstruction, and recognition. Additionally, we highlight the challenges and future opportunities for event cameras, seeking a more efficient way for machines to perceive and interact with the world.

This paper proposes an asynchronous particle filter to reconstruct high dynamic range (HDR) video from low dynamic range (LDR) frame and event data. Event cameras have the characteristics of HDR and high temporal resolution, but at the same time they discard the absolute intensity information of the image. Traditional CMOS image sensors can provide low-frequency image with intensity information. In order to fuse the two kinds of information with different frequencies, we interpolate the image according to the inter-frame events. The HDR image is obtained by combining the two with an asynchronous particle filter (PF). For the any distribution of event noise parameters, PF is not limited by the event noise distribution and has a better estimation performance. By selecting some challenging scenes from the event camera datasets ACD and IJRR, the experimental results show that our algorithm can achieve higher quality image reconsruction.

Event-based cameras, inspired by biological vision, asynchronously capture per-pixel brightness changes, producing streams of events with higher temporal resolution, dynamic range, and lower latency than conventional cameras. These advantages make event cameras promising for human pose estimation in challenging scenarios, such as motion blur and low-light conditions. However, human pose estimation with event cameras is still in its early research stages. Among major challenges is information loss from stationary parts of the human body, where the stationary parts at instances cannot trigger events. This issue, inherent to the nature of event data, cannot be resolved in a short-range event stream alone. Therefore, incorporating motion cues from a longer temporal range offers a intuitive solution. This paper proposes a joint global and local temporal modeling network (JGLTM), designed to extract essential cues from a longer temporal range to complement and refine local features for more accurate current pose prediction. Unlike existing methods that rely only on short-range temporal correspondence, this approach expands the temporal perception field to effectively provide crucial contexts for the lost information of stationary body parts at the current time instance. Extensive experiments on public datasets and the dataset proposed in this paper demonstrate the effectiveness and superiority of the proposed approach in event-based human pose estimation across diverse scenarios.

Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data.

Recent advances in event-based cameras have led to significant developments in robotics, particularly in visual simultaneous localization and mapping (VSLAM) applications. This technique enables real-time camera motion estimation and simultaneous environment mapping using visual sensors on mobile platforms. Event cameras offer several distinct advantages over frame-based cameras, including a high dynamic range, high temporal resolution, low power consumption, and low latency. These attributes make event cameras highly suitable for addressing performance issues in challenging scenarios such as high-speed motion and environments with high-range illumination. This review paper delves into event-based VSLAM (EVSLAM) algorithms, leveraging the advantages inherent in event streams for localization and mapping endeavors. The exposition commences by explaining the operational principles of event cameras, providing insights into the diverse event representations applied in event data preprocessing. A crucial facet of this survey is the systematic categorization of EVSLAM research into three key parts: event preprocessing, event tracking, and sensor fusion algorithms in EVSLAM. Each category undergoes meticulous examination, offering practical insights and guidance for comprehending each approach. Moreover, we thoroughly assess state-of-the-art (SOTA) methods, emphasizing conducting the evaluation on a specific dataset for enhanced comparability. This evaluation sheds light on current challenges and outlines promising avenues for future research, emphasizing the persisting obstacles and potential advancements in this dynamically evolving domain.

Cameras are appealing sensors for mobile robots because they are small, passive, inexpensive and provide rich information about the environment. While cameras have been used successfully on a plenitude of robots, such as autonomous cars or drones, serious challenges remain: power consumption, latency, dynamic range, and frame rate, among others. The sequences of images acquired by a camera are highly redundant (both in space and time), and both acquiring and processing such an amount of data consumes significant power. This limits the operation time of mobile robots and, moreover, defines a fundamental power-latency tradeoff. Specialized cameras designed for high-speed or high-dynamic-range scenarios are expensive, heavy, and require additional power, which prevents their use in agile mobile robots. In this thesis, we investigate event cameras as a biologically-inspired alternative to overcome the limitations of standard cameras. These neuromorphic vision sensors work in a completely different way: instead of providing a sequence of images (i.e., frames) at a constant rate, event cameras transmit only information from those pixels that undergo a significant brightness change. These pixel-level brightness changes, called events, are timestamped with micro-second resolution and transmitted asynchronously at the time they occur. Hence, event cameras are power efficient because they convey only non-redundant information, and are able to capture very high-speed motions, thus they directly address the power-latency tradeoff. Additionally, event cameras achieve a dynamic range of more than 140dB, compared to about 60dB of standard cameras, because each pixel is autonomous and operates at its own set-point. However, since the output of an event camera is fundamentally different from that of standard cameras for which computer-vision algorithms have been developed during the past fifty years, new algorithms that can deal with the asynchronous nature of the sensor and exploit its high temporal resolution are required to unlock its potential. This thesis presents algorithms for using event cameras in the context of robotics. Since event cameras are novel sensors that are being intensively prototyped and have been commercially available only recently (ca. 2008), the literature on event-based algorithms is scarce. This poses some operational challenges as well as uncountable opportunities to explore in research. This thesis focuses on exploring the possibilities that event cameras bring to some fundamental problems in robotics and computer vision, such as localization and actuation. Amongst others, this thesis provides contributions to solving the localization problem, i.e., for a robot equipped with an event camera to be able to infer its location with respect to a given map of the environment. Classical approaches for robot localization build upon lower-level vision algorithms, and so, this thesis also presents contributions in the topics of detection, extraction, and tracking of salient visual features with an event camera, whose applicability expands far beyond the localization problem. This thesis also presents contributions in the use of event cameras for actuation and closed-loop control, i.e., in endowing the robot with the capabilities to interact with the environment to fulfill a given task. Additionally, this thesis also presents the infrastructure developed to work with event cameras in a de-facto standard robotics platform. The following is a list of contributions: * Software infrastructure, consisting of publicly available drivers, calibration tools, sensor delay characterization, and the first event camera dataset and simulator tailored for 6-DOF (degrees of freedom) camera pose estimation and SLAM (Simultaneous localization and mapping). * We introduce the concept of event and provide an algorithm to compute it. The lifetime endows the events with a finite temporal extent for a proper continuous representation of events in time. * The first method to extract FAST-like visual features (i.e., interest points or corners) from the output of an event camera. The detector operates an order of magnitude faster than previous corner detectors. * The first method to extract and track features from the output of a DAVIS camera (an event camera that also outputs standard frames from the same pixel array). Using these feature tracks, we developed the first sparse, feature-based visual-odometry pipeline. * The first two methods to track the 6-DOF pose of an event camera in a known map.While the first method minimizes the reprojection error of the events and only works on black-and-white scenes consisting of line segments, the second method uses a probabilistic filtering framework that allows tracking at high speeds on natural scenes. * The first application of a continuous-time framework to estimate the trajectory of an event camera, possibly incorporating inertial measurements, showing superior performance over pose-tracking-only methods. * An application of event cameras to collision avoidance of a quadrotor, showing how event cameras can be used to control a robot with very low latency. * An application of the use of an event camera for human-vs-machine slot-car racing, showing that event-driven algorithms are power efficient and can outperform human control.

Event cameras are novel vision sensors that output pixel-level brightness changes ("events") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches (Fig. 1). We call them Focus Loss Functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras.

Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that our FE-Fusion-VPR outperforms existing frame-based, event-based and fusion-based VPR methods in most cases on Brisbane-Event-VPR and DDD20 datasets. In a word, compared to the previous works, our FE-Fusion-VPR achieves new state-of-the-art (SOTA) VPR performance on Brisbane-Event-VPR and DDD20 datasets by fusing frames and events.

Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.

Semantic segmentation technology is crucial for image processing in practical scenarios such as autonomous driving. However, semantic segmentation based on traditional cameras still faces challenges such as information loss under dynamic lighting and motion blur of high-speed moving objects. Therefore, this paper introduces event cameras with high dynamic range and high response speed, which can effectively image under harsh lighting and high-speed motion conditions, and proposes an Event-based Image Semantic Segmentation (EVISS) method based on event cameras. In the dataset section, a large-scale and high-precision annotated dataset, Carla-Semantic, was created through CARLA simulation environment to address the issue of a limited number of event camera based semantic segmentation datasets and low annotation quality. In the network design section, to address the problem of difficult feature extraction from event data with uneven distribution, the EVISS method enhances the global connectivity and context dependency of images through an improved graph Laplace formula and attention mechanism, effectively extracting high-level event features. The experimental results on the Carla-Semantic dataset and the existing method Ev-SegNet show that our EVISS method achieves 87.89% and 81.93% in MPA and mIoU evaluation metrics, respectively, surpassing the performance of the comparative methods and effectively achieving image semantic segmentation based on event cameras.