Video-based descriptors for object recognition

We describe a visual recognition system operating on a hand-held device, based on a video-based feature descriptor, and characterize its invariance and discriminative properties. Feature selection and tracking are performed in real-time, and used to train a template-based classifier during a capture phase prompted by the user. During normal operation, the system recognizes objects in the field of view based on their ranking. Severe resource constraints have prompted a re-evaluation of existing algorithms improving their performance (accuracy and robustness) as well as computational efficiency. We motivate the design choices in the implementation with a characterization of the stability properties of local invariant detectors, and of the conditions under which a template-based descriptor is optimal. The analysis also highlights the role of time as “weak supervisor” during training, which we exploit in our implementation.

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► We analyze and derive representations of objects from video.
► We integrate multi-scale detection and tracking.
► We derive a video-based feature descriptor.
► We describe a visual recognition system for a hand-held device.