Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Confidence Based updation of Motion Conspicuity in Dynamic Scenes
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Salient human detection for robot vision
Pattern Analysis & Applications
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Hi-index | 0.01 |
For artificial systems acting and perceiving in a dynamic world a core ability is to focus on aspects of the environment that can be crucial for the task at hand. Perception in autonomous systems needs to be filtered by a biologically inspired selective ability, therefore attention in dynamic settings is becoming a key research issue. In this paper we present a model for motion salience map computation based on spatiotemporal filtering. We extract a measure of coherent motion energy and select by the center-surround mechanism relevant zones that accumulate most energy and therefore contrast with surroundings in a given time slot. The method was tested on synthetic and real video sequences, supporting biological plausibility.