We present an integrated architecture in which perception and cognition interact and provide information to each other leading to improved performance in real-world situations. Our system integrates the Felzenswalb et. al. object-detection algorithm with the ACT-R cognitive architecture. The targeted task is to predict and classify pedestrian behavior in a checkpoint scenario, most specifically to discriminate between normal versus checkpoint-avoiding behavior. The Felzenswalb algorithm is a learning-based algorithm for detecting and localizing objects in images. ACT-R is a cognitive architecture that has been successfully used to model human cognition with a high degree of fidelity on tasks ranging from basic decision-making to the control of complex systems such as driving or air traffic control. The Felzenswalb algorithm detects pedestrians in the image and provides ACT-R a set of features based primarily on their locations. ACT-R uses its pattern-matching capabilities, specifically its partial-matching and blending mechanisms, to track objects across multiple images and classify their behavior based on the sequence of observed features. ACT-R also provides feedback to the Felzenswalb algorithm in the form of expected object locations that allow the algorithm to eliminate false-positives and improve its overall performance. This capability is an instance of the benefits pursued in developing a richer interaction between bottom-up perceptual processes and top-down goal-directed cognition. We trained the system on individual behaviors (only one person in the scene) and evaluated its performance across single and multiple behavior sets.
[1]
Alessandro Oltramari,et al.
Extending Cognitive Architectures with Semantic Resources
,
2011,
AGI.
[2]
Walter Warwick,et al.
Convergence and Constraints Revealed in a Qualitative Model Comparison
,
2009
.
[3]
David A. McAllester,et al.
Object Detection with Discriminatively Trained Part Based Models
,
2010,
IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4]
C. Lebiere,et al.
Instance-Based Cognitive Models of Decision-Making
,
2005
.
[5]
Christian Lebiere,et al.
The dynamics of cognition: An ACT-R model of cognitive arithmetic
,
1999,
Kognitionswissenschaft.
[6]
Trevor Darrell,et al.
Nearest-Neighbor Methods in Learning and Vision
,
2008,
IEEE Trans. Neural Networks.
[7]
John R Anderson,et al.
An integrated theory of the mind.
,
2004,
Psychological review.
[8]
John R. Anderson.
How Can the Human Mind Occur in the Physical Universe
,
2007
.
[9]
Anthony Stentz,et al.
Integrating Perception and Cognition for AGI
,
2011,
AGI.