Vision and navigation for the Carnegie-Mellon navlab
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A Swarm-Based Volition/Attention Framework for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
An agent based evolutionary approach to path detection for off-road vehicle guidance
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Off-road Path Following using Region Classification and Geometric Projection Constraints
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Emergence of attention within a neural population
Neural Networks
Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation
International Journal of Computer Vision
A Multi-agent Approach for Range Image Segmentation
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Real-time outdoor trail detection on a mobile robot
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
Appearance contrast for fast, robust trail-following
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Terrain-based sensor selection for autonomous trail following
RobVis'08 Proceedings of the 2nd international conference on Robot vision
General road detection from a single image
IEEE Transactions on Image Processing
A swarm cognition realization of attention, action selection, and spatial memory
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Stereo-based all-terrain obstacle detection using visual saliency
Journal of Field Robotics
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Neural-swarm visual saliency for path following
Applied Soft Computing
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This paper proposes a model for trail detection and tracking that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom-up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency computation based on a priori knowledge about which pixel-wise visual features are most representative of the object being sought. This paper proposes the use of the object's overall layout as the primary cue instead, as it is more stable and predictable in natural trails. Bearing in mind computational parsimony and detection robustness, this knowledge is specified in terms of perception-action rules, which control the behavior of simple agents performing as a swarm to compute the saliency map of the input image. For the purpose of tracking, multiframe evidence about the trail location is obtained with a motion-compensated dynamic neural field. In addition, to reduce ambiguity between the trail and trail-like distractors, a simple appearance model is learned online and used to influence the agents' activity. Experimental results on a large data set reveal the ability of the model to produce a success rate on the order of 97% at 20 Hz. The model is shown to be robust in situations where previous models would fail, such as when the trail does not emerge from the lower part of the image or when it is considerably interrupted. © 2012 Wiley Periodicals, Inc. © 2013 Wiley Periodicals, Inc.