Vehicle detection method based on hybrid image template
First Claim
1. A vehicle detection method for detecting a vehicle image in measured images based on hybrid image template HIT, comprising the following steps:
- Step S1;
collecting one or more vehicle images as training images;
Step S2;
utilizing an information projection algorithm to learn all of image patches for representing vehicle object in the HIT from the training images and to compute image likelihood probability distribution of this hybrid image template, wherein the HIT consists of the multiple patches with different image features which are categorized as sketch patch, texture patch, color patch and flatness patch, wherein the image likelihood probability distribution indicates occurrence probability of the HIT in conditions of the training image data;
Step S3;
detecting regions in which an image block of the HIT is present in the input testing image to acquire candidate vehicle regions in which the vehicles are located in the testing images;
Step 3-1-6;
using the patches in the HIT and the image likelihood probability to compute vehicle detection scores of the vehicle candidates region;
Step S3-1-7;
selecting the vehicle candidate regions with the maximum vehicle detection scores, comparing the maximum vehicle score with the predefined vehicle detection threshold;
if the maximum vehicle detection score is no less that the vehicle detection threshold, determining the corresponding vehicle candidate region as a vehicle object, and determining the location and detail sketch information of the vehicle object;
Step S32;
removing the determined vehicle object from the testing image and using the remaining image to detect a next vehicle object and location and detail sketch information thereof by performing above steps, repeating above steps to detect all vehicle objects and location and detail sketch information thereof with an iterative method; and
visually indicating in the input testing image the candidate regions determined to be vehicle objects.
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Abstract
The invention discloses a vehicle detection method based on hybrid image template. This method consists of the three steps. Firstly, use no less than one vehicle image for template learning. Secondly, use information projection algorithm to learn a hybrid image template from the training images for vehicle object. The hybrid image template consists of no one less than image patch. Meanwhile, calculate the likelihood probability distribution of this template. Thirdly, use the learned HIT template to detect vehicle objects from testing images. The invention is suitable to detect vehicles with various vehicle shapes, vehicle poses, time-of-day and weather conditions. Besides vehicle localization, this method can also provide the detailed description of vehicle object.
5 Citations
11 Claims
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1. A vehicle detection method for detecting a vehicle image in measured images based on hybrid image template HIT, comprising the following steps:
-
Step S1;
collecting one or more vehicle images as training images;Step S2;
utilizing an information projection algorithm to learn all of image patches for representing vehicle object in the HIT from the training images and to compute image likelihood probability distribution of this hybrid image template, wherein the HIT consists of the multiple patches with different image features which are categorized as sketch patch, texture patch, color patch and flatness patch, wherein the image likelihood probability distribution indicates occurrence probability of the HIT in conditions of the training image data;Step S3;
detecting regions in which an image block of the HIT is present in the input testing image to acquire candidate vehicle regions in which the vehicles are located in the testing images;Step 3-1-6;
using the patches in the HIT and the image likelihood probability to compute vehicle detection scores of the vehicle candidates region;Step S3-1-7;
selecting the vehicle candidate regions with the maximum vehicle detection scores, comparing the maximum vehicle score with the predefined vehicle detection threshold;
if the maximum vehicle detection score is no less that the vehicle detection threshold, determining the corresponding vehicle candidate region as a vehicle object, and determining the location and detail sketch information of the vehicle object;Step S32;
removing the determined vehicle object from the testing image and using the remaining image to detect a next vehicle object and location and detail sketch information thereof by performing above steps, repeating above steps to detect all vehicle objects and location and detail sketch information thereof with an iterative method; andvisually indicating in the input testing image the candidate regions determined to be vehicle objects. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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Specification