Patent Document ID: 20160259981
Application ID: 14392309
Patent Flag: 0

Claim One:
1. A vehicle detection method based on hybrid image template HIT, comprising the following steps: Step S 1 : no less than one vehicle image is collected as a training image; Step S 2 : an information projection algorithm is utilized to learn all of image patches in the HIT for representing vehicle object from the training images and to compute image likelihood probability distribution of this hybrid image template; Step S 3 : the HIT learned from the step S 2 is applied to detect vehicles from the input testing image and then to acquire the position of vehicles in the testing images; the Step S 3 further comprising the following sub-steps: Step S 31 : based on the HIT, the summation-maximization SUM-MAX operation is used to detect vehicle candidates with the maximum score from the input testing image, the Step S 31 further comprising the following sub-steps; Step S 311 : a Gabor wavelets with no less than one orientation are utilized to filter the testing image, and then the sketch patches with these orientations are acquired; Step S 312 : the local maximization operation is applied to the sketch image to get a revised sketch image Step S 313 : the image patches in the HIT is used to filter the testing image and to detect vehicle patch candidates; Step S 3 - 1 - 4 : the local maximization operation is applied to the obtained vehicle patch candidates to get the revised vehicle patch candidates; Step S 3 - 1 - 5 : the revised vehicle patch candidates are merged according to their relative positions and scales in the HIT, and one or more vehicle candidate regions are generated from the testing image; Step 3 - 1 - 6 : the patches in the HIT and the image likelihood probability are used to compute vehicle detection scores of the vehicle candidates region; Step S 3 - 1 - 7 : the vehicle candidate regions with the maximum vehicle detection scores is selected from all of the vehicle candidate regions; and Step S 32 : the maximum vehicle detection score is compared with a predefined vehicle detection threshold for detecting a vehicle object, and an iterative method is utilized to get all of vehicle objects in the testing image.