Abstract
In this work we present a system for detection of objects from video streams based on properties of human vision such as saccadic eye movements and selective attention. An object, in this application a car, is represented as a collection of features (horizontal and vertical edges) arranged at specific spatial locations with respect to the position of the fixation point (the central edge). The collection of conditional probabilities, that estimate the locations of the car edges given the location of the central edge, are mapped into the weights of the neural network that combines information coming from the edge detectors (bottom-up) with expectations for edge locations (top-down). During the recognition process, the system efficiently searches the space of possible segmentations by investigating the local regions of the image in a way similar to human eye movements, probing and analyzing different locations of the input at different times. In contrast to motion-based models for vehicle detection [7, 8], our approach does not rely on motion information, and the system can detect both still and moving cars in real-time. However, adding motion information should improve the accuracy.
The author performed the work while at Brown University.
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Neskovic, P., Schuster, D., Cooper, L.N. (2004). Biologically inspired recognition system for car detection from real-time video streams. In: Rajapakse, J.C., Wang, L. (eds) Neural Information Processing: Research and Development. Studies in Fuzziness and Soft Computing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39935-3_17
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DOI: https://doi.org/10.1007/978-3-540-39935-3_17
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