Selective max-pooling for object detection

• US 9,042,601 B2
• Filed: 12/16/2013
• Issued: 05/26/2015
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Wang, Xiaoyu, Zhu, Shenghuo, Yang, Ming, Lin, Yuanqing
• Primary Examiner(s): LE, BRIAN Q
• Application Number:
  US14/108,295
• Publication Number:
  US 20140270367A1
• Time in Patent Office: 526 Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06V 10/446 using Haar-like filters, e.g. using integral image techniques
• CPC Class Codes: G06V 10/464 using a plurality of salient features, e.g. bag-of-words [BoW] representations
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting

Abstract

Systems and methods are disclosed for object detection by receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; and performing selective max-pooling to choose one or more feature regions without noises.

Claims

1. A method for object detection, comprising:

receiving an image and extracting features therefrom;

applying a learning process to determine sub-regions and select predetermined pooling regions;

performing selective max-pooling to choose one or more feature regions without noises,

forming at least an object bounding box for a location;

applying a cascaded boosting classifier to each object bounding box, with each weak classifier taking a feature response of a region inside the bounding box as its input and then the region is in tum represented by a group of small sub-regions (regionlets), and

determining a permutation invariant feature operation on features extracted from regionlets as

2. The method of claim 1, comprising counting features appearing in candidate sub-regions.

3. The method of claim 1, comprising selecting sets ofregionlets from a pool of candidate regionlet groups by boosting.

4. The method of claim 1, comprising using a regionlet representation to model fine-grained spatial appearance layouts.

5. The method of claim 1, comprising aggregating feature responses of regionlets within one group to a one dimensional feature robust to local deformation.

6. The method of claim 1, comprising applying max-pooling over regionlet features as:

7. The method of claim 1, comprising normalizing regionlets using detection windows.

8. The method of claim 1, comprising normalizing regionlets by by using relative positions and sizes of the regionlets and their groups to an object bounding box.

9. The method of claim 1, comprising training with boosting regionlet features.

10. The method of claim 1, comprising using RealBoost to train cascaded classifiers for object detection.

11. A system for object detection, comprising:

a camera;

a processor coupled to the camera;

code for receiving an image and extracting features therefrom;

applying a learning process to determine sub-regions and select predetermined pooling regions; and

performing selective max-pooling to choose one or more feature regions without noises,

forming at least an object bounding box for a location;

applying a cascaded boosting classifier to each object bounding box, with each weak classifier taking a feature response of a region inside the bounding box as its input and then the region is in tum represented by a group of small sub-regions (regionlets), and

determining a permutation invariant feature operation on features extracted from regionlets as

12. The system of claim 11, comprising code for counting features appearing in candidate sub-regions.

13. The system of claim 11, comprising code for selecting sets of regionlets from a pool of candidate regionlet groups by boosting.

14. The system of claim 11, comprising code for using a regionlet representation to model fine-grained spatial appearance layouts.

15. The system of claim 11, comprising RealBoost code to train cascaded classifiers for object detection.