Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation

Rapid Deformable Object Detection using Bounding-based Techniques

Abstract : In this work we use bounding-based techniques, such as Branch-and-Bound (BB) and Cascaded Detection (CD) to efficiently detect objects with Deformable Part Models (DPMs). Instead of evaluating the classifier score exhaustively over all image locations and scales, we use bounding to focus on promising image locations. The core problem is to compute bounds that accommodate part deformations; for this we adapt the Dual Trees data structure to our problem. We evaluate our approach using DPMs. We obtain exactly the same results but can perform the part combination substantially faster; for a conservative threshold the speedup can be double, for a less conservative we can have tenfold or higher speedups. These speedups refer to the part combination process, after the unary part scores have been computed. We also develop a multiple-object detection variation of the system, where hypotheses for 20 categories are inserted in a common priority queue. For the problem of finding the strongest category in an image this can result in more than 100-fold speedups.
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download
Contributor : Iasonas Kokkinos Connect in order to contact the contributor
Submitted on : Thursday, May 10, 2012 - 10:58:45 PM
Last modification on : Friday, January 21, 2022 - 3:01:28 AM
Long-term archiving on: : Saturday, August 11, 2012 - 2:41:42 AM


Files produced by the author(s)


  • HAL Id : hal-00696120, version 1



Iasonas Kokkinos. Rapid Deformable Object Detection using Bounding-based Techniques. [Research Report] RR-7940, INRIA. 2012. ⟨hal-00696120⟩



Record views


Files downloads