Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs

• URL: http://arxiv.org/abs/2506.07164v1
• Date: Sun, 08 Jun 2025 14:30:30 GMT
• Title: Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs
• Authors: Qiong Chang, Xinyuan Chen, Xiang Li, Weimin Wang, Jun Miyazaki,
• Abstract summary: This paper presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPU.<n>Experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support.
• Score: 11.639825636679454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual images to construct an unknown environmental map and simultaneously determines its own location. It usually imposes stringent requirements on hardware power consumption, processing speed and accuracy. Currently, the ORB (Oriented FAST and Rotated BRIEF)-based SLAM systems have exhibited superior performance in terms of processing speed and robustness. However, they still fall short of meeting the demands for real-time processing on mobile platforms. This limitation is primarily due to the time-consuming Oriented FAST calculations accounting for approximately half of the entire SLAM system. This paper presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPUs. These methods optimize the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection, which is achieved by implementing a binary-level encoding strategy to determine candidate points quickly and a separable Harris detection strategy with efficient low-level GPU hardware-specific instructions. Extensive experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support. This significant improvement highlights its effectiveness and potential for real-time applications in mobile and resource-constrained environments.

Related papers

• Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach [32.91982063297922]
  We propose a novel Slow-Fast Tracking paradigm that flexibly adapts to different operational requirements, termed SFTrack.<n>The proposed framework supports two complementary modes, i.e., a high-precision slow tracker for scenarios with sufficient computational resources, and an efficient fast tracker tailored for latency-aware, resource-constrained environments.<n>Our framework first performs graph-based representation learning from high-temporal-resolution event streams, and then integrates the learned graph-structured information into two FlashAttention-based vision backbones.
  arXiv  Detail & Related papers  (2025-05-19T09:37:23Z)
• Real-Time Human Action Recognition on Embedded Platforms [10.905968330685393]
  This work tackles the real-time performance challenges of video-based human action recognition (HAR) with four contributions. One contribution is the development of RT-HARE, a real-time HAR system tailored for embedded platforms. Experimental results on an Nvidia Jetson Xavier NX platform demonstrated that RT-HARE realizes real-time HAR at a video frame rate of 30 frames per second.
  arXiv  Detail & Related papers  (2024-09-09T14:35:23Z)
• Event-based Simultaneous Localization and Mapping: A Comprehensive Survey [52.73728442921428]
  Review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. Paper categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods.
  arXiv  Detail & Related papers  (2023-04-19T16:21:14Z)
• ETAD: A Unified Framework for Efficient Temporal Action Detection [70.21104995731085]
  Untrimmed video understanding such as temporal action detection (TAD) often suffers from the pain of huge demand for computing resources. We build a unified framework for efficient end-to-end temporal action detection (ETAD) ETAD achieves state-of-the-art performance on both THUMOS-14 and ActivityNet-1.3.
  arXiv  Detail & Related papers  (2022-05-14T21:16:21Z)
• MAPLE-Edge: A Runtime Latency Predictor for Edge Devices [80.01591186546793]
  We propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes.
  arXiv  Detail & Related papers  (2022-04-27T14:00:48Z)
• ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization [71.91942002659795]
  We investigate and adapt network quantization techniques to accelerate inference and enable its use on compute limited platforms. ZippyPoint, our efficient quantized network with binary descriptors, improves the network runtime speed, the descriptor matching speed, and the 3D model size. These improvements come at a minor performance degradation as evaluated on the tasks of homography estimation, visual localization, and map-free visual relocalization.
  arXiv  Detail & Related papers  (2022-03-07T18:59:03Z)
• 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)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors [0.9988653233188148]
  We present an algorithm to detect asynchronous corners from a stream of events in real-time on embedded systems. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59 %.
  arXiv  Detail & Related papers  (2020-10-29T12:01:33Z)
• Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO [46.20949184826173]
  This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency.
  arXiv  Detail & Related papers  (2020-03-30T14:16:23Z)

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.