Efficient Incorporation of Optical Flow into Visual Motion Estimation in Tracking

2005 ◽  
pp. 167-202
Author(s):  
Gozde Unal ◽  
Anthony Yezzi ◽  
Hamid Krim
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Visual Motion

scholarly journals Neuromorphic Configurable Architecture for Robust Motion Estimation

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Guillermo Botella ◽  
Manuel Rodríguez ◽  
Antonio García ◽  
Eduardo Ros
Keyword(s):  
Visual System ◽  
Motion Estimation ◽  
Optical Flow ◽  
Visual Motion ◽  
Specific Model ◽  
Biologically Inspired ◽  
Visual Motion Perception ◽  
Illumination Changes ◽  
And Performance ◽  
Application Fields

The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM). This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.

Optimal visual motion estimation: a note

1992 ◽  
Vol 14 (9) ◽  
pp. 959-964 ◽  
Author(s):  
M.E. Spetsakis ◽  
Y. Aloimonos
Keyword(s):  
Motion Estimation ◽  
Visual Motion

scholarly journals Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 222
Author(s):  
Baigan Zhao ◽  
Yingping Huang ◽  
Hongjian Wei ◽  
Xing Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Estimation ◽  
Optical Flow ◽  
Feature Representation ◽  
Sequential Learning ◽  
Navigation Systems ◽  
Motion State ◽  
Training Schemes ◽  
Sequential Images

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches.

scholarly journals Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7969
Author(s):  
Lianen Qu ◽  
Matthew N. Dailey
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Situation Awareness ◽  
Visual Motion ◽  
Accurate Method ◽  
Estimation Methods ◽  
Single Type ◽  
Global Localization ◽  
Trajectory Estimation

Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles.

scholarly journals How multisensory neurons solve causal inference

2021 ◽  
Vol 118 (32) ◽  
pp. e2106235118
Author(s):  
Reuben Rideaux ◽  
Katherine R. Storrs ◽  
Guido Maiello ◽  
Andrew E. Welchman
Keyword(s):  
Motion Estimation ◽  
Causal Inference ◽  
Visual Motion ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Electrophysiological Recordings ◽  
Visual Motion Cues ◽  
Integrated Or ◽  
Self Motion ◽  
The Brain

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction (“congruent” neurons), while others prefer opposing directions (“opposite” neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

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