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We propose a novel object detection approach that combines the discriminative power of object category classifiers with a simple pixel level focus of attention mechanism. The pixel-level foreground/background detectors evolve to classify each pixel as either being part of an object of interest or noise. Unlike background subtraction algorithms, the decision of what is foreground is influenced by object level knowledge rather than it being an outlier to a background distribution. The approach outperforms many background subtraction techniques in challenging scenarios. Combined with the proposed focus of attention mechanism, a robust object classifier(capable of classifying known objects or rejecting noise) runs in real-time while processing 1920x1080 videos on an off-the-shelf DSP. [ABSTRACT FROM PUBLISHER]