Active shape model for vehicle modeling and re-identification

A method for re-identifying a vehicle, comprising:

• capturing an image that includes a first vehicle;
• automatically constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by:
• (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles, wherein 3D active shape models of the vehicles in the subset of vehicles are each defined by a combination of base shapes, and wherein the base shapes include surface markings and high curvature regions, the surface markings including a front windshield of a vehicle, rear window of the vehicle and side-bottom frame of the vehicle all of which are represented as 3D edge points in the active shape model, and the high curvature regions are represented as viewing direction dependent occluding contours of the vehicle;
• (b) multiplying each of the base shapes of the predetermined set by a parameter;
• (c) adding the resultant of each multiplication to form a vector that represents a shape of the vehicle in the image;
• (d) fitting the vector to the vehicle in the image;
• and
• (e) repeating steps (a)-(d) by modifying the parameters until a difference between the fit vector and the vehicle in the image is minimized, wherein a plurality of fit vectors form the outline of the vehicle in the image;
• capturing an image that includes a second vehicle;
• constructing a 3D model of the second vehicle, wherein the 3D model of the second vehicle is constructed by performing steps (a)-(e);
• and
• determining if the 3D model of the first vehicle is the same as the 3D model of the second vehicle by comparing the 3D models to each other.

A method for performing shape based control, comprising:

• receiving an image that includes a vehicle;
• and
• automatically constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by:
• (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles, wherein 3D active shape models of the vehicles in the subset of vehicles are each defined by a combination of base shapes, and wherein the base shapes include surface markings and high curvature regions, the surface markings including a front windshield of a vehicle, rear window of the vehicle and side-bottom frame of the vehicle all of which are represented as 3D edge points in the active shape model, and the high curvature regions are represented as viewing direction dependent occluding contours of the vehicle;
• (b) multiplying each of the base shapes of the predetermined set by a parameter;
• (c) adding the resultant of each multiplication to form a vector that represents a shape of the vehicle in the image;
• (d) fitting the vector to the vehicle in the image;
• and
• (e) repeating steps (a)-(d) by modifying the parameters until a difference between the fit vector and the vehicle in the image is minimized, wherein a plurality of fit vectors form the outline of the vehicle in the image;
• identifying a desired portion of the vehicle for further analysis;
• and
• causing another image that includes the vehicle to be taken, wherein the image further includes an enhanced version of the desired portion.

A system for re-identifying a vehicle, comprising:

• a first camera for capturing an image that includes a first vehicle;
• a second camera for capturing an image that includes a second vehicle;
• and
• a computer in communication with the cameras, the computer including a memory device for storing a program and a processor in communication with the memory device, the processor operative with the program to:
• receive the image that includes the first vehicle;
• automatically construct a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by:
• (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles, wherein 3D active shape models of the vehicles in the subset of vehicles are each defined by a combination of base shapes, and wherein the base shapes include surface markings and high curvature regions, the surface markings including a front windshield of a vehicle, rear window of the vehicle and side-bottom frame of the vehicle all of which are represented as 3D edge points in the active shape model, and the high curvature regions are represented as viewing direction dependent occluding contours of the vehicle;
• (b) multiplying each of the base shapes of the predetermined set by a parameter;
• (c) adding the resultant of each multiplication to form a vector that represents a shape of the vehicle in the image;
• (d) fitting the vector to the vehicle in the image;
• and
• (e) repeating steps (a)-(d) by modifying the parameters until a difference between the fit vector and the vehicle in the image is minimized, wherein a plurality of fit vectors form the outline of the vehicle in the image;
• receive the image that includes the second vehicle;
• construct a 3D model of the second vehicle, wherein the 3D model of the second vehicle is constructed by performing steps (a)-(e);
• and
• determine if the 3D model of the first vehicle is the same as the 3D model of the second vehicle by comparing the parameters of the 3D models to each other.

A non-transitory computer readable storage medium storing instructions that, when executed by a computer, cause the computer to perform a method for modeling a vehicle, the method comprising:

• receiving an image that includes a vehicle;
• and
• automatically constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by:
• (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles, wherein 3D active shape models of the vehicles in the subset of vehicles are each defined by a combination of base shapes, and wherein the base shapes include surface markings and high curvature regions, the surface markings including a front windshield of a vehicle, rear window of the vehicle and side-bottom frame of the vehicle all of which are represented as 3D edge points in the active shape model, and the high curvature regions are represented as viewing direction dependent occluding contours of the vehicle;
• (b) multiplying each of the base shapes of the predetermined set by a parameter;
• (c) adding the resultant of each multiplication to form a vector that represents a shape of the vehicle in the image;
• (d) fitting the vector to the vehicle in the image;
• and
• (e) repeating steps (a)-(d) by modifying the parameters until a difference between the fit vector and the vehicle in the image is minimized, wherein a plurality of fit vectors form the outline of the vehicle in the image.