Seeing through events: Real-time moving object sonification for visually impaired people using event-based camera

Scene sonification is a powerful technique to help Visually Impaired People (VIP) understand their surroundings. Existing methods usually perform sonification on the entire images of the surrounding scene acquired by a standard camera or on the priori static obstacles acquired by image processing algorithms on the RGB image of the surrounding scene. However, if all the information in the scene are delivered to VIP simultaneously, it will cause information redundancy. In fact, biological vision is more sensitive to moving objects in the scene than static objects, which is also the original intention of the event-based camera. In this paper, we propose a real-time sonification framework to help VIP understand the moving objects in the scene. First, we capture the events in the scene using an event-based camera and cluster them into multiple moving objects without relying on any prior knowledge. Then, sonification based on MIDI is enabled on these objects synchronously. Finally, we conduct comprehensive experiments on the scene video with sonification audio attended by 20 VIP and 20 Sighted People (SP). The results show that our method allows both participants to clearly distinguish the number, size, motion speed, and motion trajectories of multiple objects. The results show that our method is more comfortable to hear than existing methods in terms of aesthetics

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Towards Anytime Optical Flow Estimation with Event Cameras

Event cameras are capable of responding to log-brightness changes in microseconds. Its characteristic of producing responses only to the changing region is particularly suitable for optical flow estimation. In contrast to the super low-latency response speed of event cameras, existing datasets collected via event cameras, however, only provide limited frame rate optical flow ground truth, (e.g., at 10Hz), greatly restricting the potential of event-driven optical flow. To address this challenge, we put forward a high-frame-rate, low-latency event representation Unified Voxel Grid, sequentially fed into the network bin by bin. We then propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. The key component of our EVA-Flow is the stacked Spatiotemporal Motion Refinement (SMR) module, which predicts temporally-dense optical flow and enhances the accuracy via spatial-temporal motion refinement. The time-dense feature warping utilized in the SMR module provides implicit supervision for the intermediate optical flow. Additionally, we introduce the Rectified Flow Warp Loss (RFWL) for the unsupervised evaluation of intermediate optical flow in the absence of ground truth. This is, to the best of our knowledge, the first work focusing on anytime optical flow estimation via event cameras. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5ms), fastest inference (9.2ms), time-dense motion estimation (200Hz), and strong generalization. Our code will be available at https://github.com/Yaozhuwa/EVA-Flow.Comment: Code will be available at https://github.com/Yaozhuwa/EVA-Flo

FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer

Limited by hardware cost and system size, camera's Field-of-View (FoV) is not always satisfactory. However, from a spatio-temporal perspective, information beyond the camera's physical FoV is off-the-shelf and can actually be obtained "for free" from the past. In this paper, we propose a novel task termed Beyond-FoV Estimation, aiming to exploit past visual cues and bidirectional break through the physical FoV of a camera. We put forward a FlowLens architecture to expand the FoV by achieving feature propagation explicitly by optical flow and implicitly by a novel clip-recurrent transformer, which has two appealing features: 1) FlowLens comprises a newly proposed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated in the temporal dimension. 2) A multi-branch Mix Fusion Feed Forward Network (MixF3N) is integrated to enhance the spatially-precise flow of local features. To foster training and evaluation, we establish KITTI360-EX, a dataset for outer- and inner FoV expansion. Extensive experiments on both video inpainting and beyond-FoV estimation tasks show that FlowLens achieves state-of-the-art performance. Code will be made publicly available at https://github.com/MasterHow/FlowLens.Comment: Code will be made publicly available at https://github.com/MasterHow/FlowLen