Tuning Event Camera Biases Heuristic for Object Detection Applications in Staring Scenarios
- URL: http://arxiv.org/abs/2501.18788v1
- Date: Thu, 30 Jan 2025 22:27:56 GMT
- Title: Tuning Event Camera Biases Heuristic for Object Detection Applications in Staring Scenarios
- Authors: David El-Chai Ben-Ezra, Daniel Brisk,
- Abstract summary: We present a parametes for the biases of event cameras, for tasks that require small objects detection in staring scenarios.
The main purpose of the tuning is to squeeze the camera's potential, optimize its performance, and expand its detection capabilities as much as possible.
A main conclusion that will be demonstrated is that for certain desired signals, the optimal values of the camera are very far from the default values recommended by the manufacturer.
- Score: 0.0
- License:
- Abstract: One of the main challenges in unlocking the potential of neuromorphic cameras, also called 'event cameras', is the development of novel methods that solve the multi-parameter problem of adjusting their bias parameters to accommodate a desired task. Actually, it is very difficult to find in the literature a systematic heuristic that solves the problem for any desired application. In this paper we present a tuning parametes heuristic for the biases of event cameras, for tasks that require small objects detection in staring scenarios. The main purpose of the heuristic is to squeeze the camera's potential, optimize its performance, and expand its detection capabilities as much as possible. In the presentation, we translate the experimental properties of event camera and systemic constrains into mathematical terms, and show, under certain assumptions, how the multi-variable problem collapses into a two-parameter problem that can be solved experimentally. A main conclusion that will be demonstrated is that for certain desired signals, such as the one provided by an incandescent lamp powered by the periodic electrical grid, the optimal values of the camera are very far from the default values recommended by the manufacturer.
Related papers
- Autobiasing Event Cameras [0.932065750652415]
This paper utilizes the neuromorphic YOLO-based face tracking module of a driver monitoring system as the event-based application to study.
The proposed method uses numerical metrics to continuously monitor the performance of the event-based application in real-time.
The advantage of bias optimization lies in its ability to handle conditions such as flickering or darkness without requiring additional hardware or software.
arXiv Detail & Related papers (2024-11-01T16:41:05Z) - Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications [83.8743080143778]
A visual gyroscope estimates camera rotation through images.
The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results.
Here, we address these challenges by introducing a novel visual gyroscope, which combines an Efficient Multi-Mask-Filter Rotation Estor and a Learning based optimization.
arXiv Detail & Related papers (2024-04-02T13:19:06Z) - VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
We introduce a novel methodology that extends Pose Graph Optimization techniques.
We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step.
Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
arXiv Detail & Related papers (2024-03-25T17:47:03Z) - Learning Robust Multi-Scale Representation for Neural Radiance Fields
from Unposed Images [65.41966114373373]
We present an improved solution to the neural image-based rendering problem in computer vision.
The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
arXiv Detail & Related papers (2023-11-08T08:18:23Z) - E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras [18.54225086007182]
We present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras.
The proposed method is tested in a variety of rigorous experiments for different event camera models.
arXiv Detail & Related papers (2023-06-15T12:16:38Z) - Density Invariant Contrast Maximization for Neuromorphic Earth
Observations [55.970609838687864]
Contrast (CMax) techniques are widely used in event-based vision systems to estimate the motion parameters of the camera and generate high-contrast images.
These techniques are noise-intolerance and suffer from the multiple extrema problem which arises when the scene contains more noisy events than structure.
Our proposed solution overcomes the multiple extrema and noise-intolerance problems by correcting the warped event before calculating the contrast.
arXiv Detail & Related papers (2023-04-27T12:17:40Z) - Robustifying the Multi-Scale Representation of Neural Radiance Fields [86.69338893753886]
We present a robust multi-scale neural radiance fields representation approach to overcome both real-world imaging issues.
Our method handles multi-scale imaging effects and camera-pose estimation problems with NeRF-inspired approaches.
We demonstrate, with examples, that for an accurate neural representation of an object from day-to-day acquired multi-view images, it is crucial to have precise camera-pose estimates.
arXiv Detail & Related papers (2022-10-09T11:46:45Z) - Globally-Optimal Contrast Maximisation for Event Cameras [30.79931004393174]
Event cameras are bio-inspired sensors that perform well in challenging illumination with high temporal resolution.
The pixels of an event camera operate independently and asynchronously.
The flow of events is modelled by a general homographic warping in a space-time volume.
arXiv Detail & Related papers (2022-06-10T14:06:46Z) - Monitoring and Adapting the Physical State of a Camera for Autonomous
Vehicles [10.490646039938252]
We propose a generic and task-oriented self-health-maintenance framework for cameras based on data- and physically-grounded models.
We implement the framework on a real-world ground vehicle and demonstrate how a camera can adjust its parameters to counter a poor condition.
Our framework not only provides a practical ready-to-use solution to monitor and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems.
arXiv Detail & Related papers (2021-12-10T11:14:44Z) - Event-based Motion Segmentation by Cascaded Two-Level Multi-Model
Fitting [44.97191206895915]
We present a cascaded two-level multi-model fitting method for identifying independently moving objects with a monocular event camera.
Experiments demonstrate the effectiveness and versatility of our method in real-world scenes with different motion patterns and an unknown number of moving objects.
arXiv Detail & Related papers (2021-11-05T12:59:41Z) - Event-based Motion Segmentation with Spatio-Temporal Graph Cuts [51.17064599766138]
We have developed a method to identify independently objects acquired with an event-based camera.
The method performs on par or better than the state of the art without having to predetermine the number of expected moving objects.
arXiv Detail & Related papers (2020-12-16T04:06:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.