Event Density Based Denoising Method for Dynamic Vision Sensor

Feng, Yang; Lv, Hengyi; Liu, Hailong; Zhang, Yisa; Xiao, Yuyao; Han, Chengshan

doi:10.3390/app10062024

Cited by 51 publications

(66 citation statements)

References 16 publications

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“…For each circle, we search for a continuous arc with higher timestamps than all other pixels on the circle. On the inner circle (blue), the arc length l inner should be within the interval of [ 3 , 6 ], and on the outer circle (yellow), the arc length l outer should be within the interval of [ 4 , 8 ] (see Figure 5 a). Alternatively, the arc length on the inner and outer circle should be within the interval of [ 10 , 13 ] and [ 12 , 16 ], respectively (see Figure 5 b).…”

Section: Methodsmentioning

confidence: 99%

“…Since there are numerous advantages, several recent works [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] focus on processing the unconventional output of event cameras and unlocking their potentials. In event-based vision, the corner is one of the most fundamental features, and corner event tracking is usually used in many applications, such as target tracking [ 15 , 16 ], 3D Reconstruction [ 17 , 18 ] and motion estimation [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

Duo

Zhao

2021

Sensors

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Event cameras have many advantages over conventional frame-based cameras, such as high temporal resolution, low latency and high dynamic range. However, state-of-the-art event- based algorithms either require too much computation time or have poor accuracy performance. In this paper, we propose an asynchronous real-time corner extraction and tracking algorithm for an event camera. Our primary motivation focuses on enhancing the accuracy of corner detection and tracking while ensuring computational efficiency. Firstly, according to the polarities of the events, a simple yet effective filter is applied to construct two restrictive Surface of Active Events (SAEs), named as RSAE+ and RSAE−, which can accurately represent high contrast patterns; meanwhile it filters noises and redundant events. Afterwards, a new coarse-to-fine corner extractor is proposed to extract corner events efficiently and accurately. Finally, a space, time and velocity direction constrained data association method is presented to realize corner event tracking, and we associate a new arriving corner event with the latest active corner that satisfies the velocity direction constraint in its neighborhood. The experiments are run on a standard event camera dataset, and the experimental results indicate that our method achieves excellent corner detection and tracking performance. Moreover, the proposed method can process more than 4.5 million events per second, showing promising potential in real-time computer vision applications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

Duo

Zhao

2021

Sensors

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“…A solution could be to use one of the state-of-the-art denoising solutions of the literature [42], [43] during the accumulation step, that is, before creating the edge image. Doing so, however, would be computationally expensive, as the sparse and asynchronous nature of the events at that step makes hard to look for neighbour pixels states.…”

Section: B Denoising and Fillingmentioning

confidence: 99%

Real-Time Optical Flow for Vehicular Perception With Low- and High-Resolution Event Cameras

Brebion

Moreau

Davoine

2022

IEEE Trans. Intell. Transport. Syst.

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Event cameras capture changes of illumination in the observed scene rather than accumulating light to create images. Thus, they allow for applications under high-speed motion and complex lighting conditions, where traditional framebased sensors show their limits with blur and over-or underexposed pixels. Thanks to these unique properties, they represent nowadays an highly attractive sensor for ITS-related applications. Event-based optical flow (EBOF) has been studied following the rise in popularity of these neuromorphic cameras. The recent arrival of high-definition neuromorphic sensors, however, challenges the existing approaches, because of the increased resolution of the events pixel array and a much higher throughput. As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low-and high-resolution event cameras. We formulate a novel dense representation for the sparse events flow, in the form of the "inverse exponential distance surface". It serves as an interim frame, designed for the use of proven, state-of-the-art frame-based optical flow computation methods. We evaluate our approach on both low-and high-resolution driving sequences, and show that it often achieves better results than the current state of the art, while also reaching higher frame rates, 250Hz at 346×260 pixels and 77Hz at 1280×720 pixels.Index Terms-Machine vision, neuromorphic cameras, optical flow, real-time applications.• a specific pipeline-based architecture, for computing realtime optical flow using the events from low-or highresolution neuromorphic sensors; • the formulation of a novel dense "inverse exponential distance surface", that acts as the frame-based representation computed from the events, able to feed any image-based optical flow method; • a coherent choice of algorithms and methods together for all the steps up to the fast frame-based state-of-theart optical flow (with temporal smoothing to fit well with potentially noisy input events); • we finally build and share a complementary highdefinition event-based dataset of indoor sequences with high-speed movements, used as part of our evaluation. Videos accompanying this article, showing results for both low-and high-resolution data, are available at https://youtube.com/playlist?list= PLLL0eWAd6OXBRXli-tB1NREdhBElAxisD.

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“…The first type is background activity noise. It is mainly caused by thermal noise [1] and losses in transistor switching operations [8]. The useful signal distinguishes from this kind of noise by being strongly correlated in space and time.…”

Section: Event-based Image Processingmentioning

confidence: 99%

“…The second type of noise is so-called hot pixels, which generate events at a constantly high rate. In most cases, this is due to a faulty reset switch [8]. To reduce background noise, correlation filters are mainly used in a spatio-temporal plane, as, for instance, in [9].…”

Section: Event-based Image Processingmentioning

confidence: 99%

An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection

Beck

Maier

Flitter

et al. 2021

Sensors

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Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications.

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Event Density Based Denoising Method for Dynamic Vision Sensor

Cited by 51 publications

References 16 publications

An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

Real-Time Optical Flow for Vehicular Perception With Low- and High-Resolution Event Cameras

An Extended Modular Processing Pipeline for Event-Based Vision in Automatic Visual Inspection

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