High Throughput Event Filtering: The Interpolation-based DIF Algorithm Hardware Architecture

• URL: http://arxiv.org/abs/2506.05825v1
• Date: Fri, 06 Jun 2025 07:49:18 GMT
• Title: High Throughput Event Filtering: The Interpolation-based DIF Algorithm Hardware Architecture
• Authors: Marcin Kowalczyk, Tomasz Kryjak,
• Abstract summary: We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights filter and implement it on an FPGA chip.<n>Our architecture achieved a throughput of 403.39 million events per second for a sensor resolution of 1280 x 720 and 428.45 MEPS for a resolution of 640 x 480.<n>The average values of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999 depending on the dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights (DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 x 720 and 428.45 MEPS for a resolution of 640 x 480. The average values of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.

Related papers

• Interpolation-Based Event Visual Data Filtering Algorithms [0.0]
  We propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal.<n>The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 x 720 and is therefore well suited for implementation in embedded devices.
  arXiv  Detail & Related papers  (2025-07-02T10:13:20Z)
• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis [13.018482089796159]
  We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with the Sony IMX500. Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of only 72mW. This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments.
  arXiv  Detail & Related papers  (2023-12-15T15:01:41Z)
• The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization [41.58739817444644]
  The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment. We synchronize these sensors to ensure that all data is recorded simultaneously. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks.
  arXiv  Detail & Related papers  (2023-02-10T15:12:40Z)
• Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm [0.0]
  Neuromorphic vision is a rapidly growing field with numerous applications in the perception systems of autonomous vehicles. There is a significant amount of noise in the event stream due to the sensors working principle. We present a novel algorithm based on an IIR filter matrix for filtering this type of noise and a hardware architecture that allows its acceleration.
  arXiv  Detail & Related papers  (2022-07-02T15:18:53Z)
• Super-resolution GANs of randomly-seeded fields [68.8204255655161]
  We propose a novel super-resolution generative adversarial network (GAN) framework to estimate field quantities from random sparse sensors. The algorithm exploits random sampling to provide incomplete views of the high-resolution underlying distributions. The proposed technique is tested on synthetic databases of fluid flow simulations, ocean surface temperature distributions measurements, and particle image velocimetry data.
  arXiv  Detail & Related papers  (2022-02-23T18:57:53Z)
• hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow [0.0]
  Event-based vision sensors produce asynchronous event streams with high temporal resolution based on changes in the visual scene. Existing solutions for calculating optical flow from event data fail to capture the true direction of motion due to the aperture problem. We present a hardware realization of the fARMS algorithm allowing for real-time computation of true flow on low-power, embedded platforms.
  arXiv  Detail & Related papers  (2021-12-13T16:27:17Z)
• Are we ready for beyond-application high-volume data? The Reeds robot perception benchmark dataset [3.781421673607643]
  This paper presents a dataset, called Reeds, for research on robot perception algorithms. The dataset aims to provide demanding benchmark opportunities for algorithms, rather than providing an environment for testing application-specific solutions.
  arXiv  Detail & Related papers  (2021-09-16T23:21:42Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset [50.8779574716494]
  Event cameras are bio-inspired vision sensors which measure per pixel brightness changes. They offer numerous benefits over traditional, frame-based cameras, including low latency, high dynamic range, high temporal resolution and low power consumption. To foster the development of 3D perception and navigation algorithms with event cameras, we present the TUM-VIE dataset.
  arXiv  Detail & Related papers  (2021-08-16T19:53:56Z)
• DeepMark++: Real-time Clothing Detection at the Edge [55.41644538483948]
  We propose a single-stage approach to deliver rapid clothing detection and keypoint estimation. Our solution is based on a multi-target network CenterNet, and we introduce several powerful post-processing techniques to enhance performance. Our most accurate model achieves results comparable to state-of-the-art solutions on the DeepFashion2 dataset.
  arXiv  Detail & Related papers  (2020-06-01T04:36:57Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)

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.