DaDe: Delay-adaptive Detector for Streaming Perception

• URL: http://arxiv.org/abs/2212.11558v2
• Date: Fri, 23 Dec 2022 03:26:01 GMT
• Title: DaDe: Delay-adaptive Detector for Streaming Perception
• Authors: Wonwoo Jo, Kyungshin Lee, Jaewon Baik, Sangsun Lee, Dongho Choi, Hyunkyoo Park
• Abstract summary: In real-time environment, surrounding environment changes when processing is over. Streaming perception is proposed to assess the latency and accuracy of real-time video perception. We develop a model that can reflect processing delays in real time and produce the most reasonable results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognizing the surrounding environment at low latency is critical in autonomous driving. In real-time environment, surrounding environment changes when processing is over. Current detection models are incapable of dealing with changes in the environment that occur after processing. Streaming perception is proposed to assess the latency and accuracy of real-time video perception. However, additional problems arise in real-world applications due to limited hardware resources, high temperatures, and other factors. In this study, we develop a model that can reflect processing delays in real time and produce the most reasonable results. By incorporating the proposed feature queue and feature select module, the system gains the ability to forecast specific time steps without any additional computational costs. Our method is tested on the Argoverse-HD dataset. It achieves higher performance than the current state-of-the-art methods(2022.12) in various environments when delayed . The code is available at https://github.com/danjos95/DADE

Related papers

• SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio [17.811771707446926]
  We show that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. We provide our trained model, SONNET, which is runnable in real-time and works on novel data out of the box for many real data applications.
  arXiv  Detail & Related papers  (2024-11-20T10:23:21Z)
• MTD: Multi-Timestep Detector for Delayed Streaming Perception [0.5439020425819]
  Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. This paper propose the Multi- Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings.
  arXiv  Detail & Related papers  (2023-09-13T06:23:58Z)
• Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving [0.0]
  Environmental perception is a key element of autonomous driving. In this paper, we investigate the best trade-off between detection quality and latency for real-time perception in autonomous vehicles. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.
  arXiv  Detail & Related papers  (2023-08-09T21:39:10Z)
• Neural Laplace Control for Continuous-time Delayed Systems [76.81202657759222]
  We propose a continuous-time model-based offline RL method that combines a Neural Laplace dynamics model with a model predictive control (MPC) planner. We show experimentally on continuous-time delayed environments it is able to achieve near expert policy performance.
  arXiv  Detail & Related papers  (2023-02-24T12:40:28Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Object-based Illumination Estimation with Rendering-aware Neural Networks [56.01734918693844]
  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene.
  arXiv  Detail & Related papers  (2020-08-06T08:23:19Z)
• Non-Stationary Delayed Bandits with Intermediate Observations [10.538264213183076]
  Online recommender systems often face long delays in receiving feedback, especially when optimizing for some long-term metrics. We introduce the problem of non-stationary, delayed bandits with intermediate observations. We develop an efficient algorithm based on UCRL, and prove sublinear regret guarantees for its performance.
  arXiv  Detail & Related papers  (2020-06-03T09:27:03Z)
• Towards Streaming Perception [70.68520310095155]
  We present an approach that coherently integrates latency and accuracy into a single metric for real-time online perception. The key insight behind this metric is to jointly evaluate the output of the entire perception stack at every time instant. We focus on the illustrative tasks of object detection and instance segmentation in urban video streams, and contribute a novel dataset with high-quality and temporally-dense annotations.
  arXiv  Detail & Related papers  (2020-05-21T01:51:35Z)

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.