Abstract:
Surveillance cameras typically study the exact same scene day and night, year after year, but utilize general pedestrian detectors trained to detect pedestrians from any ...Show MoreMetadata
Abstract:
Surveillance cameras typically study the exact same scene day and night, year after year, but utilize general pedestrian detectors trained to detect pedestrians from any viewing angle. By specializing the detector to the scene it is used on, its need for computational resources can be reduced and thereby its carbon footprint and operational cost. We propose a (optionally straightened) height normalizing image transform, (S)HeNIT, to be used as a preprocessing step to a CNN detector. It removes the pixel height variations of pedestrians moving on a ground plane. Therefore, the visual variation is reduced, and the available pixels are more evenly redistributed between close and distant pedestrians. This allows the detector to be specialized to a specific scene by fine-tuning and pruning it on rendered, scene specific, synthetic data. The transform also allows for lower resolution input images to be used, making recognition more difficult, which is desirable from a privacy standpoint.
Published in: 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Date of Conference: 29 November 2022 - 02 December 2022
Date Added to IEEE Xplore: 24 November 2022
ISBN Information: