Globally Optimal Contrast Maximisation for Event-Based Motion Estimation

Liu, Daqi; Parra, Álvaro; Chin, Tat-Jun

doi:10.1109/cvpr42600.2020.00638

Cited by 45 publications

(40 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have shown that contrast maximization and entropy minimization can cause the loss function to have multiple extremes in some situations [33,34]. In this study, we clearly showed that when using contrast maximization and entropy minimization to perform homographic motion estimation for a plane, the loss function has multiple extremes and the motion estimation result deviates from the true value.…”

Section: Discussionmentioning

confidence: 56%

“…In response to this problem, Liu et al [33] proposed a method for finding the global maximum solution of a rotational motion using a branch and bound (BnB) approach for contrast maximization. Peng et al [34] also proposed a method for finding the global maximum solution using BnB and achieved planar motion estimation from an event camera mounted downward on an automatic guided vehicle (AGV).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

Ozawa

Sekikawa

Saitō

2022

Sensors

View full text Add to dashboard Cite

Event cameras are bio-inspired sensors that have a high dynamic range and temporal resolution. This property enables motion estimation from textures with repeating patterns, which is difficult to achieve with RGB cameras. Therefore, motion estimation of an event camera is expected to be applied to vehicle position estimation. An existing method, called contrast maximization, is one of the methods that can be used for event camera motion estimation by capturing road surfaces. However, contrast maximization tends to fall into a local solution when estimating three-dimensional motion, which makes correct estimation difficult. To solve this problem, we propose a method for motion estimation by optimizing contrast in the bird’s-eye view space. Instead of performing three-dimensional motion estimation, we reduced the dimensionality to two-dimensional motion estimation by transforming the event data to a bird’s-eye view using homography calculated from the event camera position. This transformation mitigates the problem of the loss function becoming non-convex, which occurs in conventional methods. As a quantitative experiment, we created event data by using a car simulator and evaluated our motion estimation method, showing an improvement in accuracy and speed. In addition, we conducted estimation from real event data and evaluated the results qualitatively, showing an improvement in accuracy.

show abstract

Section: Discussionmentioning

confidence: 56%

Section: Related Workmentioning

confidence: 99%

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

Ozawa

Sekikawa

Saitō

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Such a frameless approach is proposed in [9], where is employed a plane-fitting method on short temporal windows of events, to determine their motion in the visual scene. Other works [27], [28], [29] propose contrast maximization schemes as proxies for computing optical flow, by evaluating the sharpness of motion-compensated images of accumulated events. More recently, authors such as [30], [31] exploit spiking neural networks for a full bio-inspired EBOF estimation.…”

Section: Event-based Optical Flow (Ebof)mentioning

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.

View full text Add to dashboard Cite

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.

show abstract

“…This kernel replaces the bilinear voting with a 3x3 Gaussian kernel with σ = 1 pixel [11]. The weights of the kernel are calculated based on (4) for the immediate 9 neighbours in a 3x3 grid, following that a Gaussian smoothing (see (8)).…”

Section: ) 3x3 Gaussian Kernelmentioning

confidence: 99%

“…Extending this heuristic to event influence, it can be said that 99.7% of the event influence for x ′ k is expended for events x ′ up to 3 standard deviations of x ′ k . As such this method replaces the Kronecker delta with a single-step Gaussian function as in ( 4), but calculating the Gaussian weight for neighbouring pixels rσ from x ′ k as shown in (11) where 1 ≤ r ≤ 3.…”

Section: B Pseudo Fully Connected Gaussianmentioning

confidence: 99%

Aggregation Functions for Simultaneous Attitude and Image Estimation With Event Cameras at High Angular Rates

Soh

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

For fast-moving event cameras, projection of events onto the image frame exhibits smearing of events analogous to high motion blur. For camera attitude estimation, this presents a causality dilemma where motion prior is required to unsmear events, but an image prior is required to estimate motion. This dilemma is typically circumvented by including an IMU to provide motion priors. However, IMUs limited dynamic range of ±2000 • /s are shown to be insufficient for high angular rate rotorcrafts. Contrast Maximization is an event-only optimization framework that computes the optimal motion compensation parameter while generating an event image simultaneously. This paper analyses the performance of existing aggregation functions of the contrast maximization framework and proposes a nonconvolution-based aggregation function that outperforms existing implementations. The use of discrete event images for optimizers is discussed, demonstrating alternate avenues of the framework to exploit. The effect of motion blur in motion-compensated images is defined and studied for Contrast Maximisation at high angular rates. Lastly, the framework is applied to rotation datasets with angular rates exceeding 2000 • /s to demonstrate high angular rate motion estimation without motion priors.

show abstract

Globally Optimal Contrast Maximisation for Event-Based Motion Estimation

Cited by 45 publications

References 25 publications

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

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

Aggregation Functions for Simultaneous Attitude and Image Estimation With Event Cameras at High Angular Rates

Contact Info

Product

Resources

About