Abstract
In this paper, we introduce an approach to multi-camera, multi-object detection that builds on low-level object localization with the targeted use of high-level pedestrian detectors. Low-level detectors often identify a small number of candidate locations, but suffer from false positives. We introduce a method of pedestrian verification, which takes advantage of geometric and scene information to (1) drastically reduce the search space in both the spatial and scale domains, and (2) select the camera(s) with the highest likelihood of providing accurate high-level detection. The proposed framework is modular and can incorporate a variety of existing detection methods. Compared to recent methods on a benchmark dataset, our method improves detection performance by 2.4 %, while processing more than twice as fast.
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Spurlock, S., Souvenir, R. (2015). Pedestrian Verification for Multi-Camera Detection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_21
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DOI: https://doi.org/10.1007/978-3-319-16865-4_21
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