A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds

• URL: http://arxiv.org/abs/2103.00857v1
• Date: Mon, 1 Mar 2021 09:16:18 GMT
• Title: A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds
• Authors: Xiao Huang, Hong Qiao, Hui Li and Zhihong Jiang
• Abstract summary: Rapid accurate and robust detection of looming objects in moving backgrounds is a significant and challenging problem for robotic visual systems. Inspired by the neural circuit elementary vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (AS) The proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making.
• Score: 19.93930316898735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making.

Related papers

• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• Spatio-Temporal Feedback Control of Small Target Motion Detection Visual System [9.03311522244788]
  This paper develops a visual system withtemporal-temporal feedback to detect small target motion. The proposed visual system is composed of two complementary spatial neuronalworks. Experimental results demonstrate that the system is more competitive than existing methods in detecting small targets.
  arXiv  Detail & Related papers  (2022-11-18T10:10:48Z)
• Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions [65.84090965167535]
  We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
  arXiv  Detail & Related papers  (2022-06-29T18:47:05Z)
• A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds [19.93930316898735]
  This paper proposes a bioinspired neural network based on a new neuro-based motion filtering and multiform 2-D spatial filtering. It can estimate motion direction accurately via only two signals and respond to small targets of different sizes and velocities. It can also extract the information of motion direction and motion accurately energy and rapidly.
  arXiv  Detail & Related papers  (2021-03-01T08:44:27Z)
• Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors [99.94079901071163]
  This paper presents a Dynamic Vision Sensor (DVS) based system for reasoning about high speed motion. We consider the case of a robot at rest reacting to a small, fast approaching object at speeds higher than 15m/s. We highlight the results of our system to a toy dart moving at 23.4m/s with a 24.73deg error in $theta$, 18.4mm average discretized radius prediction error, and 25.03% median time to collision prediction error.
  arXiv  Detail & Related papers  (2020-11-18T17:55:07Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• TENet: Triple Excitation Network for Video Salient Object Detection [57.72696926903698]
  We propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning at the beginning of training. Our semi-curriculum learning design enables the first online strategy for VSOD, which allows exciting and boosting saliency responses during testing without re-training.
  arXiv  Detail & Related papers  (2020-07-20T08:45:41Z)
• Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds [6.670414650224423]
  This paper investigates the fruit fly textitDrosophila motion vision pathways and presents computational modelling based on physiological researches. The proposed visual system model features-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. Experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets.
  arXiv  Detail & Related papers  (2020-07-02T05:15:31Z)
• Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds [22.850519892606716]
  We have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories.
  arXiv  Detail & Related papers  (2020-05-06T10:04:23Z)
• A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments [8.645725394832969]
  Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro robots. We propose an STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses.
  arXiv  Detail & Related papers  (2019-12-29T03:10:36Z)

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.