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Soft-Cascade Learning with Explicit Computation Time Considerations

Abstract : This paper presents a novel framework for learning a soft-cascade detector with explicit computation time considerations. Classically, training techniques for soft-cascade detectors select a set of weak classifiers and their respective thresholds, solely to achieve the desired detection performance without any regard to the detector response time. Nevertheless, since computation time performance is of utmost importance in many time-constrained applications , this work divulges an optimization approach that aims to minimize the mean cascade response time, given a desired detection performance, fixed beforehand. The resulting problem is NP-Hard, therefore finding an optimal threshold vector can be very time-consuming, especially when building a soft-cascade detector of long length. An efficient local search procedure is presented that deals with long-length detectors. Our evaluations on two challenging public datasets confirm that a faster cascade detector can be learned while maintaining similar detection performances .
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Contributor : Francisco Rodolfo BARBOSA-ANDA Connect in order to contact the contributor
Submitted on : Thursday, March 8, 2018 - 10:36:21 AM
Last modification on : Tuesday, October 25, 2022 - 11:58:11 AM
Long-term archiving on: : Saturday, June 9, 2018 - 1:30:39 PM


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  • HAL Id : hal-01726292, version 1


Francisco Rodolfo Barbosa-Anda, Frédéric Lerasle, Cyril Briand, Alhayat Ali Mekonnen. Soft-Cascade Learning with Explicit Computation Time Considerations. 2018 IEEE Winter Conference on Applications of Computer Vision, Mar 2018, Lake Tahoe, United States. ⟨hal-01726292⟩



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