Fast object detection method based on deformable part model (DPM)

• US 9,846,821 B2
• Filed: 07/28/2015
• Issued: 12/19/2017
• Est. Priority Date: 08/22/2014
• Status: Active Grant

• Current Assignee: Zhejiang Shenghui Lighting Company Limited (Tongxiang Shenghui Lighting Electrical Company Limited)
• Original Assignee: Zhejiang Shenghui Lighting Company Limited (Tongxiang Shenghui Lighting Electrical Company Limited)
• Inventors: Li, Maolin, Shen, Jinxiang
• Primary Examiner(s): Woldemariam, Aklilu
• Application Number:
  US15/024,278
• Publication Number:
  US 20160210530A1
• Time in Patent Office: 875 Days
• Field of Search: 382103, 382106, 382115, 382154, 382159, 348 43, 348 47, 348152
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/422 for representing the structure of the pattern or shape of an object therefor
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/49 Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes

Abstract

A fast object detection method based on deformable part model (DPM) is provided. The method includes importing a trained classifier for object detection, receiving an image frame from a plurality of frames in a video captured by a camera, and identifying regions possibly containing at least one object via objectness measure based on Binarized Normed Gradients (BING). The method also includes calculating Histogram of Oriented Gradients (HOG) feature pyramid of the image frame, performing DPM detection for the identified regions possibly containing the at least one object, and labeling the at least one detected object using at least one rectangle box via non-maximum suppression (NMS). Further, the method includes processing a next frame from the plurality of frames in the captured video until the video ends and outputting object detection results.

Claims

1. A fast object detection method based on a deformable part model (DPM) implemented by an object detection system, comprising:

importing a trained classifier for object detection;

receiving an image frame from a plurality of image frames in a video captured by a camera;

identifying candidate regions in the image frame that may contain at least one object via objectness measure based on Binarized Normed Gradients (BING), the candidate regions being a subpart of the received image frame, wherein the objectness measure quantifies how likely it is for a region in the image frame to contain an object as opposed to backgrounds;

calculating Histogram of Oriented Gradients (HOG) feature pyramid of the image frame;

performing DPM detection for the identified candidate regions that may contain the at least one object based on the calculated HOG feature pyramid;

labeling the at least one detected object using at least one rectangle box via non-maximum suppression (NMS);

processing a next frame from the plurality of frames in the captured video until the video ends; and

outputting object detection results,

wherein;

the fast object detection method is integrated with a LED lighting device;

a model for an object with n parts is formally defined by (n+2)-tuple (F0, P1 , . . . , Pn, b), wherein F0 is a root filter, Pi is a model for a i-th part and b is a real-valued bias term, n being an integer; and

provided that a location of each filter in a feature pyramid is (p0 , . . . , pn), and pi=(xi, yi, li) specifies a level and position of the i-th filter, a score of a window is defined by;

2. The method according to claim 1, further including:

processing each pixel in the first N levels of the HOG feature pyramid in each identified region that may contain the at least one object, wherein N is an integer greater than 1;

calculating a score of a root filter, scores of part filters, and a deformation cost, respectively; and

calculating a score of a window.

3. The method according to claim 2, further including:

when the score of the window is greater than a preset threshold, recording a current location.

4. The method according to claim 1, wherein calculating Histogram of Oriented Gradients (HOG) feature pyramid of the image frame further includes:

calculating the HOG feature pyramid of the image frame using a look-up-table to speed up the feature calculation.

5. A fast object detection system based on a deformable part model (DPM), comprising:

a camera configured to capture a video;

a receiving module configured to receive an image frame from a plurality of frames in the video captured by the camera;

an objectness measure module configured to identify candidate regions in the image frame that may contain at least one object via the objectness measure based on Binarized Normed Gradients (BING), the candidate regions being a subpart of the received image frame, wherein the objectness measure quantifies how likely it is for a region in the image frame to contain an object as opposed to backgrounds;

a Histogram of Oriented Gradients (HOG) module configured to calculate a HOG feature pyramid of the image frame;

a DPM detection module configured to perform DPM detection for the identified candidate regions that may contain the at least one object based on the calculated HOG feature pyramid; and

a label module configured to label the at least one detected object using at least one rectangle box via non-maximum suppression (NMS),

wherein;

the fast object detection system is integrated with a LED lighting device;

a model for an object with n parts is formally defined by (n+2)-tuple (F0, P1 , . . . , Pn, b), wherein F0 is a root filter, Pi is a model for a i-th part and b is a real-valued bias term, n being an integer; and

provided that a location of each filter in a feature pyramid is (p0 , . . . , pn), and pi=(xi, yi, li) specifies a level and position of the i-th filter, a score of a window is defined by;

6. A The system according to claim 5, wherein the DPM detection module is further configured to:

process each pixel in the first N levels of the HOG feature pyramid in each identified region that may contain the at least one object, wherein N is an integer greater than 1; and

calculate a score of a root filter, scores of part filters, a deformation cost, and a score of a window, respectively.

7. The system according to claim 6, wherein:

when the score of the window is greater than a preset threshold, a current location is recorded.

8. The system according to claim 5, wherein:

the HOG module is further configured to calculate the HOG feature pyramid of the image frame using a look-up-table to speed up feature calculation.

9. The system according to claim 5, wherein:

when a rate of object detection is lower than a pre-set number for a certain location, the LED lighting device is configured to increase a light intensity or change a light orientation of a corresponding area that is being video recorded.