Patent Description:
Nowadays, the field of computer based image and video analysis has progressed to tackle monitoring task in an automated manner. Especially, vehicle monitoring based on image processing has become an interesting topic. To monitor a vehicle, it is of particular interest to recognize the license plate mounted on the vehicle. In some embodiments, the appearance of the license plate in the image may be subject to a deformation due to, for example, a tilt of the license plate. To better recognize the license plate in the image, it is desired to provide a system and method to efficiently identify the tilt angle of the license plate and correct the license plate thereafter.

<CIT> and <CIT> disclose methods for image pre-processing for determining and correcting the tilt angle of a license plate captured by a camera.

An aspect of the present disclosure relates to a system for license plate recognition. The system may include at least one storage medium including a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor is directed to cause the system to obtain an image including a license plate mounted on a vehicle, and identify at least one feature point associated with the vehicle. The at least one processor is also directed to cause the system to determine an approximate angle range within which a tilt angle of the license plate is located based on the at least one feature point, and determine the tilt angle of the license plate within the approximate angle range. The at least one processor is further directed to cause the system to perform a tilt correction on the license plate based on the tilt angle of the license plate.

In some embodiments, the at least one feature point associated with the vehicle may include a first feature point located at the license plate and at least one second feature point located at the vehicle and outside the license plate.

In some embodiments, the first feature point located at the license plate may include a center point of the license plate.

In some embodiments, the at least one second feature point located at the vehicle and outside the license plate may include a second feature at a left boundary of the vehicle and a second feature at a right boundary of the vehicle.

In some embodiments, to determine an approximate angle range within which a tilt angle of the license plate is located, the at least one processor is directed to cause the system to determine an approximate tilt angle based on a position relation between the first feature point and the at least one second feature point, and determine the approximate angle range based on the approximate tilt angle.

In some embodiments, the approximate tilt angle may be the median of the approximate angle range.

In some embodiments, to determine the tilt angle of the license plate within the approximate angle range, the at least one processor is directed to cause the system to identify a plurality of reference points of the license plate, and determine a parameter value associated with the plurality of reference points of the license plate for each of a plurality of candidate angles in the approximate angle range. The at least one processor is further directed to designate one of the plurality of candidate angles as the tilt angle of the license plate based on the plurality of parameter values.

In some embodiments, the parameter value associated with the plurality of reference points of the license plate may be a variance of a plurality of projection points that are projected by the plurality of reference points.

In some embodiments, the at least one processor is further directed to cause the system to reconstruct the corrected license plate in response to a determination that the license plate needs reconstruction.

In some embodiments, the reconstruction of the corrected license plate may include an adjustment of a size of the corrected license plate.

Another aspect of the present disclosure relates to a method for license plate recognition. The method may be implemented on a computing device including at least one processor and at least one storage medium. The method may include obtaining an image including a license plate mounted on a vehicle, and identifying at least one feature point associated with the vehicle. The method may also include determining an approximate angle range within which a tilt angle of the license plate is located based on the at least one feature point, and determining the tilt angle of the license plate within the approximate angle range. The method may further include performing a tilt correction on the license plate based on the tilt angle of the license plate.

In order to illustrate the technical solutions related to the embodiments of the present disclosure, brief introduction of the drawings referred to in the description of the embodiments is provided below. Obviously, drawings described below are only some examples or embodiments of the present disclosure. Those having ordinary skills in the art, without further creative efforts, may apply the present disclosure to other similar scenarios according to these drawings. Unless stated otherwise or obvious from the context, the same reference numeral in the drawings refers to the same structure and operation.

As used in the disclosure and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" when used in the disclosure, specify the presence of stated steps and elements, but do not preclude the presence or addition of one or more other steps and elements.

Some modules of the system may be referred to in various ways according to some embodiments of the present disclosure, however, any number of different modules may be used and operated in a client terminal and/or a server. These modules are intended to be illustrative, not intended to limit the scope of the present disclosure. Different modules may be used in different aspects of the system and method.

According to some embodiments of the present disclosure, flow charts are used to illustrate the operations performed by the system. It is to be expressly understood, the operations above or below may or may not be implemented in order. Conversely, the operations may be performed in inverted order, or simultaneously. Besides, one or more other operations may be added to the flowcharts, or one or more operations may be omitted from the flowchart.

Technical solutions of the embodiments of the present disclosure be described with reference to the drawings as described below. It is obvious that the described embodiments are not exhaustive and are not limiting. Other embodiments obtained, based on the embodiments set forth in the present disclosure, by those with ordinary skill in the art without any creative works are within the scope of the present disclosure.

An aspect of the present disclosure relates to systems and methods for recognizing a license plate in an image of a vehicle. The systems may determine an approximate angle range of the license plate and locate a tilt angle of the license plate within the approximate angle range. The approximate angle range may be an angle range covering an approximate tilt angle of the license plate. The approximate tilt angle may relate to an orientation of the vehicle in the image and be determined according to at least one feature point (e.g., the feature point(s) of the license plate, the feature point(s) of other parts of the vehicle other than the license plate). The systems may further perform a tilt correction on the license plate based on the tilt angle of the license plate. In some embodiments, the systems may further reconstruct the corrected license plate to facilitate the subsequent process of license plate recognition.

<FIG> is a schematic diagram illustrating an exemplary license plate recognition system <NUM> according to some embodiments of the present disclosure. The license plate recognition system <NUM> may recognize a licence plate of a vehicle in an image. As illustrated in <FIG>, the license plate recognition system <NUM> may include an imaging device <NUM>, an image processing device <NUM>, a terminal <NUM>, a storage <NUM>, a network <NUM>, and/or any other suitable component in accordance with various embodiments of the disclosure.

The imaging device <NUM> may be a device configured to capture one or more images. As used in this application, an image may be a still image, a video, a stream video, or a video frame obtained from a video. The one or more images may include an image of a vehicle <NUM> with a license plate <NUM> on it. The vehicle <NUM> in the image may be presented with various views, such as, a front view, a lateral view, a perspective view, etc..

In some embodiments, the imaging device <NUM> may be a camera, a smart phone, a tablet, a laptop, a video gaming console equipped with a web camera, etc. The camera may be a static camera, a pan-tilt-zoom camera, a moving camera, a stereoscopic camera, a structured light camera, a time-of-flight camera, etc. Specifically, the imaging device <NUM> may be a camera equipped with a time-of-flight device, a Kinect sensor, a 3D laser scanner, a photographic scanner, etc. The types of the imaging device <NUM> may be different, depending on the installation location and/or the type of objects to be captured.

The image(s) generated by the imaging device <NUM> may be stored in the storage <NUM>, and/or sent to the image processing device <NUM>, or the terminal <NUM> via the network <NUM>.

The image processing device <NUM> may process an image generated by the imaging device <NUM> or retrieved from another component in the license plate system <NUM> (e.g., the storage <NUM>, the terminal <NUM>). For example, the image processing device <NUM> may identify a position or an orientation of the vehicle <NUM> in the image. Additionally, the image processing <NUM> may identify the license plate <NUM> on the vehicle <NUM> and recognize the text information on the license plate <NUM>. Specifically, the image processing device <NUM> may perform a vehicle recognition algorithm to position the vehicle <NUM> in the image, and further determine a tilt angle of the license plate <NUM> according to one or more feature points of the vehicle <NUM>.

In some embodiments, the image processing device <NUM> may generate a control signal based on, for example, a feature of an object (e.g., the vehicle <NUM>), an image of an object, a video of an object, or the like, or a combination. The control signal may be used to control the imaging device <NUM>. For example, the image processing device <NUM> may generate a control signal to control the imaging device <NUM> (e.g., a camera) to track an object and obtain an image of the object.

In some embodiments, the image processing device <NUM> may include one or more processing engines (e.g., single-core processing engine(s) or multi-core processor(s)). Merely by way of example, the image processing device <NUM> may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction-set computer (RISC), a microprocessor, or the like, or any combination thereof.

The terminal <NUM> may be connected to or communicate with the image processing device <NUM>. The terminal <NUM> may allow one or more operators (e.g., a security officer, a law enforcement officer, etc.) to control the production and/or display of the data (e.g., the image captured by the imaging device <NUM>) on a display. The terminal <NUM> may include an input device, an output device, a control panel, a display (not shown in <FIG>), or the like, or a combination thereof.

Exemplary input device may include a keyboard, a touch screen, a mouse, a remote controller, a wearable device, or the like, or a combination thereof. For example, the input device may include alphanumeric and other keys that may be inputted via a keyboard, a touch screen (e.g., with haptics or tactile feedback, etc.), a speech input, an eye tracking input, a brain monitoring system, or any other comparable input mechanism. The input information received through the input device may be communicated to the image processing device <NUM> via the network <NUM> for further processing. Exemplary input device may further include a cursor control device, such as a mouse, a trackball, or cursor direction keys to communicate direction information and command selections to, for example, the image processing device <NUM> and to control cursor movement on display or another display device.

A display may be configured to display the data received (e.g., the image captured by the imaging device <NUM>). The data may include data before and/or after data processing, a request for input or parameter relating to image acquisition and/or processing, or the like, or any combination thereof. Exemplary display may include a liquid crystal display (LCD), a light emitting diode (LED)-based display, a flat panel display or curved screen (or television), a cathode ray tube (CRT), or the like, or a combination thereof.

The storage <NUM> may store data and/or instructions. The data may include an image (e.g., an image obtained by the imaging device <NUM>), relevant information of the image, etc. In some embodiments, the storage <NUM> may store data and/or instructions that the image processing device <NUM> may execute or use to perform exemplary methods described in the present disclosure. In some embodiments, the storage <NUM> may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM, etc. In some embodiments, the storage <NUM> may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

The network <NUM> may facilitate communications between various components of the license plate recognition system <NUM>. The network <NUM> may be a single network, or a combination of various networks. Merely by way of example, the network <NUM> may include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public telephone switched network (PSTN), a BluetoothTM network, a ZigBeeTM network, a near field communication (NFC) network, a global system for mobile communications (GSM) network, a code-division multiple access (CDMA) network, a time-division multiple access (TDMA) network, a general packet radio service (GPRS) network, an enhanced data rate for GSM evolution (EDGE) network, a wideband code division multiple access (WCDMA) network, a high speed downlink packet access (HSDPA) network, a long term evolution (LTE) network, a user datagram protocol (UDP) network, a transmission control protocol/Internet protocol (TCP/IP) network, a short message service (SMS) network, a wireless application protocol (WAP) network, a ultra wide band (UWB) network, an infrared ray, or the like, or any combination thereof. The network <NUM> may also include various network access points, e.g., wired or wireless access points such as one or more base stations <NUM> or Internet exchange points through which a data source may connect to the network <NUM> in order to transmit information via the network <NUM>. It should be noted that the descriptions above of the license plate recognition system <NUM> is provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, various variations and modifications may be conducted under the guidance of the present disclosure. However, those variations and modifications shall not depart the scope of the present disclosure. In some embodiments, the storage <NUM> may be combined with the image processing device <NUM> as a single device. Similar modifications should fall within the scope of the present disclosure.

It should be noted that the descriptions above in relation to the license plate recognition system <NUM> is provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, various variations and modifications may be conducted under the guidance of the present disclosure. However, those variations and modifications do not depart the scope of the present disclosure. For example, part or all of the image data generated by the imaging device <NUM> may be processed by the terminal <NUM>. As another example, the imaging device <NUM> and the image processing device <NUM> may be implemented in one single device configured to perform the functions of the imaging device <NUM> and the image processing device <NUM> described in this disclosure. As still another example, the terminal <NUM>, and the storage <NUM> may be combined with or part of the image processing device <NUM> as a single device. Similar modifications should fall within the scope of the present disclosure.

<FIG> is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. The image processing device <NUM> and/or the terminal <NUM> may be implemented using one or more computing devices <NUM> and/or one or more portions of computing devices <NUM>.

The computing device <NUM> may be used to implement any part of the data transmission as described herein. For example, the image processing device <NUM> may be implemented on the image processing device <NUM>, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the image processing as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.

The computing device <NUM>, for example, may include COM ports <NUM> connected to and from a network connected thereto to facilitate data communications. The computing device <NUM> may also include a processor <NUM>, in the form of one or more processors, for executing program instructions. The exemplary computer platform may include an internal communication bus <NUM>, a program storage and data storage of different forms, such as, and a read only memory (ROM) <NUM>, a random access memory (RAM) <NUM>, or a disk <NUM>, for various data files to be processed and/or transmitted by the computer. The exemplary computer platform may also include program instructions stored in the ROM <NUM>, RAM <NUM>, and/or any other type of non-transitory storage medium to be executed by the processor <NUM>. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device <NUM> also includes an I/O component <NUM>, supporting input/output between the computing device <NUM> and outside components. The computing device <NUM> may also receive programming and data via network communications.

The processor <NUM> may execute instructions and/or data to perform one or more functions described in the present disclosure. For example, the processor <NUM> may determine a tilt angle of a license plate of a vehicle in an image. In some embodiments, the processor <NUM> may include one or more processors (e.g., single-core processor(s) or multi-core processor(s)). Merely by way of example, the processor <NUM> may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction-set computer (RISC), a microprocessor, or the like, or any combination thereof.

Merely for illustration, only one processor <NUM> is described in the computing device <NUM>. However, it should be noted that the computing device <NUM> in the present disclosure may also include multiple processors, thus operations and/or method steps that are performed by one processor <NUM> as described in the present disclosure may also be jointly or separately performed by the multiple CPUs/processors. For example, if in the present disclosure the processor <NUM> of the computing device <NUM> executes both step A and step B, it should be understood that step A and step B may also be performed by two different CPUs/processors jointly or separately in the computing device <NUM> (e.g., the first processor executes step A and the second processor executes step B, or the first and second processors jointly execute steps A and B).

The ROM <NUM>, the RAM <NUM>, and/or the disk <NUM> may store data and/or instructions that may perform one or more functions described in the present disclosure. For example, the ROM <NUM>, the RAM <NUM>, and/or the disk <NUM> may store instructions executed by the processor <NUM> to determine whether a visible light image and an infrared image relating to a same scene are registered. As another example, the ROM <NUM>, the RAM <NUM>, and/or the disk <NUM> may store instructions executed by the processor <NUM> to recognize a license plate of a vehicle in an image. In some embodiments, the RAM <NUM> may include a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), or the like, or any combination thereof. In some embodiments, the ROM <NUM> may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically-erasable programmable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM, or the like, or any combination thereof. In some embodiments, the disk <NUM> may include a magnetic disk, an optical disk, a solid-state drive, a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, or the like, or any combination thereof. In some embodiments, the ROM <NUM>, the RAM <NUM>, and/or the disk <NUM> may include a data storage, an application, etc. In some embodiments, the data storage may be any hardware or software for storing data, including a circuitry, a program, etc. In some embodiments, the application may include any application that may be installed in the computing device <NUM> for querying data.

The I/O <NUM> may support an input/output between the computing device <NUM> and an outside component. Merely by way of example, the I/O <NUM> may include a display, a keypad/keyboard, or the like, or any combination thereof. The display may be an output device for presenting information in visual form. In some embodiments, the display may include a liquid crystal display (LCD) panel, a light emitting diode display (LED) panel, an organic light emitting diodes (OLED) panel, a cathode ray tube (CRT) display, a plasma display, a touchscreen, a simulated touchscreen, the like, or any combination thereof. The keypad/keyboard may be an input device for typing in information from a user. In some embodiments, the keypad/keyboard may include a standard alphanumeric keyboard, a simplified alphanumeric keyboard, a flexible keyboard, a handheld keyboard, a software keyboard, an on-screen keyboard, a laser projection keyboard, a sense board, or the like, or any combination thereof.

The COM ports <NUM> may be connected to a network to facilitate data communications. In some embodiments, the COM ports <NUM> may be an interface with the network <NUM> and/or one or more components in the license plate recognition system <NUM>. In some embodiments, the COM ports <NUM> may be any type of wired or wireless network interface. Merely by way of example, the COM ports <NUM> may include a cable network interface, a wireline network interface, an optical fiber network interface, a telecommunications network interface, an intranet interface, an internet interface, a local area network (LAN) interface, a wide area network (WAN) interface, a wireless local area network (WLAN) interface, a metropolitan area network (MAN) interface, a wide area network (WAN) interface, a public telephone switched network (PSTN) interface, a Bluetooth network interface, a ZigBee network interface, a near field communication (NFC) network interface, or the like, or any combination thereof. In some embodiments, the COM ports <NUM> may be implemented according to programming and/or computer language(s). The COM ports <NUM> may include circuitry for coupling the computing device <NUM> to one or more networks, and is constructed for use with one or more communication protocols and technologies including, global system for mobile communications (GSM), code-division multiple access (CDMA), time-division multiple access (TDMA), general packet radio service (GPRS), enhanced data rate for GSM evolution (EDGE), wideband code division multiple access (WCDMA), high speed downlink packet access (HSDPA), long term evolution (LTE), user datagram protocol (UDP), transmission control protocol/Internet protocol (TCP/IP), short message service (SMS), wireless application protocol (WAP), ultra wide band (UWB), IEEE <NUM> worldwide interoperability for microwave access (WiMax), session initiated protocol/real-time transport protocol (SIP/RTP), or any of a variety of other wireless communication protocols.

The internal communication bus <NUM> may transfer information and/or data between one or more components of the computing device <NUM>. For example, the internal communication bus <NUM> may connect the processor <NUM> with a storage (e.g., the RAM <NUM>, the ROM <NUM>, etc.) for exchanging information and/or data. In some embodiments, the internal communication bus <NUM> may include a hardware component and/or a software implementation. For example, the internal communication bus <NUM> may include a wire, an optical fiber, a cable, a communication protocol, or the like, or any combination thereof.

<FIG> is a schematic diagram illustrating exemplary components of an exemplary user device according to some embodiments of the present disclosure. As illustrated in <FIG>, the user device <NUM> may include a communication platform <NUM>, a display <NUM>, a graphic processing unit (GPU) <NUM>, a central processing unit (CPU) <NUM>, an I/O port <NUM>, a memory <NUM>, and a storage <NUM>. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the user device <NUM>. In some embodiments, a mobile operating system <NUM> (e.g., iOS™, Android™, Windows Phone™) and one or more applications <NUM> may be loaded into the memory <NUM> from the storage <NUM> in order to be executed by the processor <NUM>. The user device <NUM> may be an embodiment of the terminal <NUM>. The applications <NUM> may include an image player for receiving and displaying an image provided by the imaging device <NUM> through the network <NUM>.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform(s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

<FIG> is a block diagram of an exemplary image processing device <NUM> according to some embodiments of the present disclosure. The image processing device <NUM> may include an obtaining module <NUM>, an identification module <NUM>, a determination module <NUM>, a calculation module <NUM>, a reconstruction module <NUM>, and an extraction module <NUM>.

The obtaining module <NUM> may obtain an image from other components of the license plate recognition system <NUM>. The obtained image may include a vehicle with a license plate mounted on it.

The identification module <NUM> may identify one or more feature points in an image. For example, the identification module <NUM> may identify one or more feature point in an image of a vehicle including, a center point of a license plate of the vehicle, a boundary point of the vehicle, etc. In some embodiments, the identification module <NUM> may identify the region of the license plate and further identify the center point of the license plate as well as other feature points of the license plate.

The determination module <NUM> may determine a tilt angle of a license plate in an image of a vehicle. In some embodiments, the determination module <NUM> may determine an approximate tilt angle of the license plate based on one or more feature points associated with the vehicle. Further, the determination module <NUM> may determine an angle range that covers the approximate tilt angle of the license plate. The angle range may be further used to determine the (exact) tilt angle of the license plate.

The calculation module <NUM> may determine a tilt angle of a license plate. In some embodiments, the calculation module <NUM> may select a plurality of candidate angles from an angle range, and rotate the license plate by each of the plurality of candidate angles. After each rotation, the calculation module <NUM> may calculate a parameter value associated with a plurality of reference points of the license plate and designate one candidate angle as the tilt angle of the license plate based on the plurality of parameter values. In some embodiments, the parameter value may be a variance of a plurality of projection points that are projected by the plurality of reference points of the license plate.

The reconstruction module <NUM> may reconstruct an image or a portion thereof. For example, the reconstruction module <NUM> may reconstruct a license plate in an image of a vehicle by resizing the license plate (e.g., adjusting the width/height of the license plate). In some embodiments, the reconstruction module <NUM> may also perform a judgement of whether the image or a portion thereof needs to be reconstructed.

The extraction module <NUM> may extract information from a license plate. The information of the license plate may include the characters in the license plate that indicates the identification of a vehicle. In some embodiments, the extraction module <NUM> may extract the characters in the license plate by an Optical Character Recognition (OCR) technique or a license plate character recognition (LPCR) algorithm by using, for example, a neural network.

<FIG> is a flow chart illustrating an exemplary process <NUM> for license plate recognition according to some embodiments of the present disclosure. The process <NUM> may be executed by the license plate recognition system <NUM>. For example, the process <NUM> may be implemented as a set of instructions stored in the storage ROM <NUM> or RAM <NUM>. The processor <NUM> and/or the modules in <FIG> may execute the set of instructions, and when executing the instructions, the processor <NUM> and/or the modules may be configured to perform the process <NUM>. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process <NUM> may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process <NUM> as illustrated in <FIG> and described below is not intended to be limiting.

In <NUM>, the obtaining module <NUM> may obtain an image including a vehicle having a license plate. In some embodiments, the image may be received from any suitable storage device (e.g., the storage <NUM> illustrated in <FIG>, the DISK <NUM> illustrated in <FIG>, the storage <NUM> illustrated in <FIG>), any suitable imaging device (e.g., the imaging device <NUM> illustrated in <FIG>), or any terminal device (e.g., the terminal <NUM>).

The vehicle may include a car, a bus, a truck, etc. The license plate may be mounted on the front and/or the back of the vehicle. The license plate may be a rectangular plate with a numeric or alphanumeric ID of the vehicle. The license plate may have a background color (e.g., blue, green, white) that is different from the color of the numeric or alphanumeric ID.

In some embodiments, due to various views of the vehicle in the image, the license plate presented in the image may be subject to a deformation. For example, the license plate presented in the image may be oblique, and/or the shape of the license plate presented in the image may have the shape of a diamond. For brevity, the deformation of the license plate presented in the image may be indicated by a tilt angle of the license plate. The tilt angle of the license plate may depend on the orientation of the vehicle in the image. In the case when the vehicle is presented in the image with its front view, the license plate as well as the numeric or alphanumeric ID on the license plate may be horizontally positioned in the image, and thus the tilt angle of the license plate may be regarded as <NUM>.

In some embodiments, the vehicle presented in the image may be included in a reference zone. The reference zone may be represented by a box with a specific shape, such as, a rectangular box, a circular box, an oval box. In some embodiment, the reference zone may be generated by a vehicle recognition process. In some embodiments, the reference zone may be determined manually.

In <NUM>, the feature identification module <NUM> may identify at least one feature point associated with the vehicle. The at least one feature point may include one or more points (pixels) of a specific part (e.g. a headlight, a back light, a rear-view mirror, a tyre, the logo, the front windshield, the back windshield, the license plate) of the vehicle in the image. For example, the at least one feature point may include a point of a headlight (e.g., the center point of the headlight, an edge point of the headlight), a point of a rear-view mirror (e.g., an edge point of the rear-view mirror), an edge point of the head of the vehicle, a point of the logo of the vehicle, etc. The identification of the at least one feature point associated with the vehicle may be further used to determine, for example, the orientation of the vehicle, and/or the tilt angle of the license plate.

In some embodiments, the identification of the at least one feature point may be performed by various techniques including an edge detection technique, a color-based technique, a machine learning technique, a texture-based technique, or the like, or any combination thereof. For example, the edge detection technique may be utilized to detect the left/right boundary of the head of the vehicle. The color-based technique may be utilized to detect the license plate that has a distinctive color in the image. The texture-based technique may be utilized to detect the license plate with a distinctive pattern in the image. The machine learning technique may be utilized to detect an object of interest via a trained network, e.g., a convolutional neural network (CNN). The object of interest may include the logo of the vehicle, the license plate of the vehicle, the headlight of the vehicle, etc. More descriptions of the identification of a feature point may be found elsewhere in the present disclosure (e.g., <FIG> and the description thereof).

In <NUM>, the determination module <NUM> may determine, based on the at least one feature point, an angle range of the license plate. The angle range of the license plate may be an approximate range within which the tilt angle of the license plate may be located.

The angle range of the license plate may relate to the orientation of the vehicle in the image. For example, if the vehicle faces towards the right side of the image, the license plate in the image may tilt to the top right of the image (e.g., the license plate in <FIG>), and thus the angle range of the license plate may be within the range of <NUM> degree to <NUM> degrees. If the vehicle faces towards the left side of the image, the license plate in the image may tilt to the top left of the image (e.g., the license plate in <FIG>), and thus the angle range of the license plate may be within the range of -<NUM> degrees to <NUM> degree.

In some embodiments, in order to determine the angle range of the license plate, the determination module <NUM> may first determine a reference angle θ of the license plate based on the at least one feature point. The reference angel θ may be an approximate tilt angle of the license plate.

As described elsewhere in the present disclosure, the at least one feature point may include one or more points of a specific part of the vehicle in the image. For illustration purpose, the description of the determination of the reference angle θ of the license plate may take one or more feature points of the license plate as an example. It should be noted that the determination of the reference angle θ of the license plate below are merely some examples or implementations. For persons having ordinary skills in the art, the determination of the reference angle θ of the license plate may be performed according to other feature points, such as, one or more feature points of the logo of the vehicle.

In some embodiments, the one or more feature points of the license plate may include the center point of the license plate. The determination module <NUM> may determine a first horizontal distance between the center point of the license plate and the left boundary of the head of the vehicle, and a second horizontal distance between the center point of the license plate and the right boundary of the head of the vehicle. The left/right boundary of the head of the vehicle may be defined with different representations in different scenarios. For example, the left/right boundary of the head of the vehicle may be defined with the center/edge point of the left/right headlight of the vehicle, provided that the left/right headlight can be detected in the image. As another example, the left/right boundary of the head of the vehicle may be defined with the outermost point of the head of the vehicle, provided that the left/right headlight cannot be detected in the vehicle. For brevity, the first horizontal distance may be denoted as ΔLl, and the second horizontal distance may be denoted as ΔLr. If the first horizontal distance is greater than the second horizontal distance, the determination module <NUM> may determine that the vehicle faces towards the right side of the image. If the first horizontal distance is smaller than the second horizontal distance, the determination module <NUM> may determine that the vehicle faces towards the left side of the image.

Further, the determination module <NUM> may calculate the reference angle θ of the license plate based on the first horizontal distance and the second horizontal distance according to formula (<NUM>). <MAT> where f is a function that indicates the relation between the first horizontal distance, the second horizontal distance, and the reference angle θ of the license plate. Specifically, the relation among the first horizontal distance, the second horizontal distance, and the reference angle θ of the license plate may be denoted as a mapping relation between a ratio of the first horizontal distance to the second horizontal distance, i.e., ΔL<NUM>/ΔLr, and the reference angle θ of the license plate according to formula (<NUM>).

In some embodiments, the function f may be predetermined and stored in a storage device (e.g., the storage <NUM>) of the license plate recognition system <NUM>. In some embodiments, the function f may be a neural network model. When the first horizontal distance ΔLl and the second horizontal distance ΔLr are input into the neural network model, the reference angle θ may be generated as the result of the neural network model.

In some embodiments, the one or more feature points of the license plate may include multiple points of the license plate. The multiple points of the license plate may include one or more corner points of the license plate including the top left corner point, the top right corner point, the bottom left corner point, and the bottom right corner point of the license plate. The determination module <NUM> may determine the orientation of the vehicle in the image and/or the reference angle θ of the license plate based on the position information of the multiple points of the license plate. For example, the determination module <NUM> may determine the vertical coordinates of the top left corner point and the top right corner point of the license plate. If the vertical coordinate of the top left corner point is greater than the vertical coordinate of the top right corner point, the determination module <NUM> may determine that the vehicle faces towards the left side of the image. If the vertical coordinate of the top left corner point is smaller than the vertical coordinate of the top right corner point, the determination module <NUM> may determine that the vehicle faces towards the right side of the image. Further, the determination module <NUM> may calculate an included angle between the horizontal direction and the line connecting the top left corner point and the top right corner point of the license plate, and calculate the reference angle θ of the license plate based on a mapping relation between the reference angle θ and the included angle.

After the reference angle θ of the license plate is determined, the determination module <NUM> may further determine the angle range of the license plate. The angle range of the license plate may be set such that the reference angle θ may be included in it. In some embodiments, the angle range of the license plate may be set to be (θ - Δθ, θ + Δθ) such that the reference angle θ may be the median of the angle range. Δθ may be any value, such as, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, etc..

In <NUM>, the calculation module <NUM> may determine a tilt angle of the license plate within the angle range. In some embodiments, the calculation module <NUM> may select a plurality of candidate angles within the angle range and determine a confidence score associated with each of the plurality of candidate angles. Then, the calculation module <NUM> may designate the candidate angle whose confidence score satisfies a condition as the tilt angel of the license plate. In some embodiments, the calculation module <NUM> may rotate the license plate by a selected candidate angle, and determine the confidence score associated with the selected candidate angle based on the distribution of the points of the rotated license plate. Specifically, the confidence score associated with the selected candidate angle may be represented by a variance of the coordinates of the points of the rotated license plate, or a variance of the coordinates of projected points that are projected by the points of the rotated license plate. More descriptions regarding the determination of the tilt angle of the license plate may be found elsewhere in the present disclosure, e.g., <FIG> and the description thereof.

In <NUM>, the calculation module <NUM> may perform a tilt correction on the license plate based on the tilt angle.

The tilt correction may include rotating the license plate by the tilt angle. In some embodiments, after rotation, the left boarder and the right boarder of the license plate may be parallel to or substantially parallel to the vertical direction. In some embodiments, after rotation, the upper boarder and the lower boarder of the license plate may be parallel to or substantially parallel to the horizontal direction. In some embodiments, the tilt correction may include an interpolation operation (e.g., a bilinear interpolation) to calculate a point value in the tilted license plate.

Additionally or alternatively, the tilt correction may include performing a shear transformation (e.g., a horizontal shear, a vertical shear) on the rotated license plate. For example, the calculation module <NUM> may perform a horizontal shear on the rotated license plate whose upper boarder and lower boarder are parallel to or substantially parallel to the horizontal direction. As another example, the calculation module <NUM> may perform a vertical shear on the rotated license plate whose left boarder and right boarder are parallel to or substantially parallel to the vertical direction. As still another example, the calculation module <NUM> may perform a shear mapping operation on the rotated license plate according to a specific shear matrix.

In some embodiments, after the tilt correction, the corrected license plate may have the shape of a rectangle, or the like.

In <NUM>, the reconstruction module <NUM> may determine whether a reconstruction of the corrected license plate is needed. As used herein, the reconstruction of the corrected license plate may refer to an operation to adjust one or more parameters of the corrected license plate to generate a reconstructed license plate. In some embodiments, the reconstructed license plate may facilitate the subsequent processing(s) on the license plate (e.g., recognizing the characters in the license plate). The adjusted one or more parameters of the corrected license plate may include, for example, the resolution of the corrected license plate, the size of the corrected license plate, or the like, or a combination thereof.

In some embodiments, the reconstruction module <NUM> may determine whether to reconstruct the corrected license plate based on the width of the corrected license plate and/or the ratio of the width to the height of the reconstructed license plate. For example, if the ratio of the width to the height of the reconstructed license plate is less than a threshold r, and the width of the license plate is less than a threshold w, the reconstruction module <NUM> may determine that the license plate needs to be reconstructed. Otherwise, the reconstruction module <NUM> may determine that the license plate does not need to be reconstructed. The threshold r and/or the threshold w may be predetermined and stored in a storage device (e.g., the storage <NUM>) of the license plate recognition system <NUM>. In response to the determination that the reconstruction of the corrected license plate is needed, the process <NUM> may proceed to <NUM>; otherwise, the process <NUM> may proceed to <NUM>.

In <NUM>, the reconstruction module <NUM> may perform the reconstruction of the corrected license plate. In some embodiments, the reconstruction module <NUM> may employ various reconstruction techniques including, for example, a super-resolution reconstruction (SR) technique, a maximum a posteriori (MAP) based technique, or the like, or any combination thereof. In some embodiments, the reconstruction of the corrected license plate may include adjusting the ratio of the width to the height of the reconstructed license plate by, for example, enlarging the width of the corrected license plate. Under different scenarios, the width of the corrected license plate may be enlarged by <NUM>, <NUM>, <NUM>, etc., times.

In <NUM>, the extraction module <NUM> may extract information of the license plate from the acquired license plate (the corrected license plate or the reconstructed license plate). The information of the license plate may include the characters in the license plate that indicates the identification of the vehicle. In some embodiments, the characters in the license plate may be extracted by an Optical Character Recognition (OCR) technique. In some embodiments, the characters in the license plate may be extracted by a license plate character recognition (LPCR) algorithm by using, for example, a neural network.

It should be noted that the above description of process <NUM> is merely provided for the purposes of illustration, and not intended to be understood as the only embodiment. For persons having ordinary skills in the art, various variations and modifications may be conduct under the teaching of some embodiments of the present disclosure. In some embodiments, some operations may be reduced or added. However, those variations and modifications may not depart from the protecting of some embodiments of the present disclosure. For example, one or more other optional operations (e.g. pre-processing operation) may be added in the process <NUM>. For example, the identification module <NUM> may preprocess the image (e.g., enhance the contrast of the image) before identifying the feature(s) associated with the vehicle.

<FIG> is a flow chart illustrating an exemplary process <NUM> for feature point identification according to some embodiments of the present disclosure. The process <NUM> may be executed by the license plate recognition system <NUM>. For example, the process <NUM> may be implemented as a set of instructions stored in the storage ROM <NUM> or RAM <NUM>. The processor <NUM> and/or the modules in <FIG> may execute the set of instructions, and when executing the instructions, the processor <NUM> and/or the modules may be configured to perform the process <NUM>. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process <NUM> may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process <NUM> are performed as illustrated in <FIG> and described below is not intended to be limiting. In some embodiments, the operation <NUM> in the process <NUM> may be performed according to the process <NUM>.

In <NUM>, the identification module <NUM> may determine a candidate region associated with a license plate in an image of a vehicle. The candidate region may be an approximate representation of the license plate that indicates an approximate position, an approximate shape, etc., of the license plate in the image. In some embodiments, the identification module <NUM> may determine the candidate region via a morphology-based technique, a neural network technique (e.g. a trained Darknet model), an object segmentation technique (e.g., an edge-based technique), or the like, or any combination thereof.

In <NUM>, the identification module <NUM> may expand the candidate region such that the license plate may be completely included in the expanded region. In some embodiments, the candidate region may be expanded according to one or more boundary points of the candidate region. A boundary point of the candidate region may be the point with at least one of: the maximum horizontal coordinate, the minimum horizontal coordinate, the maximum vertical coordinate, or the minimum vertical coordinate in the candidate region. Then, the identification module <NUM> may expand the candidate region to cover all the points whose horizontal coordinates are within the range between the maximum horizontal coordinate and the minimum horizontal coordinate, and vertical coordinates are within the range between the maximum vertical coordinate and the minimum vertical coordinate. In some embodiments, the candidate region may be expanded according to a user input that indicates the area of the expanded region.

In <NUM>, the identification module <NUM> may position the license plate in the expanded region according to at least one characteristic of the license plate. The at least one characteristic of the license plate may include the color of the license plate (e.g. the color of the characters in the license plate, the background color of the license plate), the edges of the license plate (e.g., the vertical edge line, the horizontal edge line), the texture of the license plate, or the like, or any combination thereof.

In some embodiments, to better position the license plate in the expanded region, a pre-processing operation may be performed on the image. For example, the image may be converted into a gray image via the pro-processing operation. Optionally, the noise in the gray image may be further reduced by a Median Filtering and the contrast of the gray image may be further enhanced by a histogram equalization. Then, the edges of the license plate may be extracted by an edge-based algorithm. Specifically, the edge-based algorithm may include identifying the horizontal/vertical lines in the gray image, and designating the prominent horizontal/vertical lines as the horizontal/vertical edge lines of the license plate.

In <NUM>, the identification module <NUM> may determine at least one feature point of the license plate in the image.

In some embodiments, the at least one feature point of the license plate may include the center point of the license plate. In some embodiments, the at least one feature point of the license plate may include multiple points of the license plate. The multiple points of the license plate may include one or more corner points of the license plate including the top left corner point, the top right corner point, the bottom left corner point, and the bottom right corner point of the license plate. In some embodiments, the at least one feature point of the license plate may be further used to determine the orientation of the vehicle or the angle range of the license plate as described in the process <NUM>.

It should be noted that the above description of process <NUM> is merely provided for the purposes of illustration, and not intended to be understood as the only embodiment. For persons having ordinary skills in the art, various variations and modifications may be conduct under the teaching of some embodiments of the present disclosure. In some embodiments, some operations may be reduced or added. However, those variations and modifications may not depart from the protecting of some embodiments of the present disclosure. For example, the feature point of other parts in the image other than the license plate may be similarly determined according to the process <NUM>. The other parts in the image may include the logo of the vehicle, the front windshield of the vehicle, the back windshield of the vehicle, or the like, or any combination thereof. Further, the feature point of other parts may also be used to determine the orientation of the vehicle or the angle range of the license plate according to a similar process as the process <NUM>.

<FIG> is a flow chart illustrating an exemplary process <NUM> for determining a tilt angle of a license plate according to some embodiments of the present disclosure. The process <NUM> may be executed by the license plate recognition system <NUM>. For example, the process <NUM> may be implemented as a set of instructions stored in the storage ROM <NUM> or RAM <NUM>. The processor <NUM> and/or the modules in <FIG> may execute the set of instructions, and when executing the instructions, the processor <NUM> and/or the modules may be configured to perform the process <NUM>. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process <NUM> may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process <NUM> are performed as illustrated in <FIG> and described below is not intended to be limiting. In some embodiments, the operation <NUM> may be performed according to the process <NUM>.

In <NUM>, the calculation module <NUM> may obtain an angle range of a license plate in an image. In some embodiments, the angle range may be an approximate tilt range within which the tilt angle of the license plate may be located.

In <NUM>, the calculation module <NUM> may identify a plurality of reference points of the license plate. In some embodiments, the plurality of reference points may include one or more feature points of the license plate as described elsewhere in the present disclosure, e.g., the top left corner point, the top right corner point, the bottom left corner point, the bottom right corner point, and the center point of the license plate. Additionally or alternatively, the plurality of reference points may include one or more points of the characters in the license plate, such as the top point of each character in the license plate. Additionally or alternatively, the plurality of reference points may include one or more points of the edge line(s) of the license plate, such as the point of the upper edge line the license plate.

In some embodiments, the identification of the plurality of reference points may be performed by various techniques including an edge detection technique, a color-based technique, a machine learning technique, a texture-based technique, or the like, or any combination thereof.

In <NUM>, the calculation module <NUM> may determine a plurality of projection points of the plurality of reference points for each of a plurality of candidate angles in the angle range.

The plurality of candidate angles in the angle range may be the points selected from the angle range randomly or according to a predetermined rule. For example, the plurality of candidate angles may be evenly distributed within the angle range. The angle difference between each adjacent candidate angles may be any value, such as, <NUM>°, <NUM>°, <NUM>°, etc. For illustration purpose, assuming that the angle range is from <NUM>° to <NUM>° and the angle difference is <NUM>°, the plurality of candidate angles may be <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>° and <NUM>°.

For each of the plurality of candidate angles, the calculation module <NUM> may rotate the license plate by that candidate angle, and then determine the plurality of projection points based on the plurality of reference points. In some embodiments, each projection point may be generated by projecting one of the plurality of reference points onto a line.

For brevity, the vertical line passing through the center point of the license plate may be described as an example. It should be noted that the line described below are merely an example or implementation. For persons having ordinary skills in the art, the vertical line may be replaced by any other suitable lines, such as the horizontal line passing through the center point of the license plate, or any other vertical lines parallel to the vertical line passing through the center point.

Referring to <FIG>, two points, A and B, are examples of two of the reference points of a tilted license plate <NUM>. The points A and B are the top left point and the top right point of the tilted license plate <NUM>, respectively. The y axis overlaps the line that passes through the center point of the tilted license plate <NUM>. In this case, after the calculation module <NUM> rotates the license plate <NUM> by each of the plurality of candidate angles, the point A may be projected onto the y axis to generate its projection point y<NUM> on the axis, and the point B may be projected onto the y axis to generate its projection point y<NUM> on the y axis.

In <NUM>, the calculation module <NUM> may determine a variance of the plurality of projection points for each of the plurality of candidate angles in the angle range. In some embodiments, the variance of the plurality of projection points may be represented by the variance of the positions of the plurality of projection points. For example, in the case that the projection points are generated by projecting the reference points onto the y axis, the variance of the plurality of projection points for a specific candidate angle may be represented by the variance of the vertical coordinates of the plurality of projection points. For example, as shown in <FIG>, the vertical coordinates of the plurality of projection points may include the vertical coordinates of the projection point y<NUM> and the projection point y<NUM>.

In <NUM>, the calculation module <NUM> may designate one of the plurality of candidates angles as a tilt angle of the license plate based on the plurality of variances. In some embodiments, the variance of the plurality of projection points may reflect the uniformity of the projection points. A smaller variance of the projection points may indicate a higher uniformity of the projection points, and thus a higher confidence score for the projection points. The calculation module <NUM> may determine the minimum variance among the plurality of variances, and designate the candidate angle corresponding to the minimum variance as the tilt angle of the license plate. For example, the <FIG> shows a license plate <NUM> corresponding to the minimum variance. In <FIG>, the projection point of the top left point A' and the projection point of the top right point B' are both located at y<NUM> on the y axis, having a higher uniformity than the projection points y<NUM> and y<NUM> in <FIG>.

It should be noted that the above description of the process <NUM> is merely provided for the purposes of illustration, and not intended to be understood as the only embodiment. For persons having ordinary skills in the art, various variations and modifications may be conduct under the teaching of some embodiments of the present disclosure. In some embodiments, some operations may be reduced or added. However, those variations and modifications may not depart from the protecting of some embodiments of the present disclosure. In some embodiments, instead of determining the projection points of the plurality of reference points, the calculation module <NUM> may perform a curve fitting (e.g., a polynomial curve fitting) to the plurality of reference points for each candidate angle. For example, the calculation module <NUM> may fit the top points of the characters in the license plate to a linear line. The slope of the linear line may relate to the tilt angle of the license plate.

<FIG> are schematic diagrams illustrating different tilt angles of a license plate according to some embodiments of the present disclosure.

In <FIG>, the vehicle <NUM> is presented with its front view, and the license plate <NUM> is horizontally positioned in the image. The vehicle <NUM> is included in a rectangular box <NUM> (i.e., a reference zone). The first horizontal distance ΔLl is represented by the horizontal distance between the center point of the license plate <NUM> and the left boundary of the head of the vehicle. The second horizontal distance ΔLr is represented by the horizontal distance between the center point of the license plate <NUM> and the right boundary of the head of the vehicle. In this case, the first horizontal distance is equal to the second horizontal distance, i.e., ΔLl = ΔLr.

In <FIG>, the vehicle <NUM> faces towards the left side of the image, and the license plate <NUM> tilts to the top left of the image. Different from <FIG>, the first horizontal distance is smaller than the second horizontal distance, i.e., ΔLl < ΔLr, and the tilt angle of the license plate <NUM> is within the range of -<NUM> degrees to <NUM> degree.

In <FIG>, the vehicle <NUM> faces towards the right side of the image, and the license plate <NUM> tilts to the top right of the image. Different from <FIG> and <FIG>, the first horizontal distance is greater than the second horizontal distance, i.e., ΔLl > ΔLr, and the tilt angle of the license plate <NUM> is within the range of <NUM> degree to <NUM> degrees.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure, and are within the spirit and scope of the exemplary embodiments of this disclosure.

For example, the terms "one embodiment," "an embodiment," and "some embodiments" mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure.

Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a "module," "unit," "component," "device," or "system.

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, it is to be understood that such detail is solely for that purpose. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Claim 1:
A system, comprising:
an obtaining module configured to obtain an image including a license plate mounted on a vehicle;
an identification module configured to identify feature points associated with the vehicle, wherein the feature points include a center point of the license plate, a left boundary of the head of the vehicle and a right boundary of the head of the vehicle;
a determination module configured to determine an approximate angle range within which a tilt angle of the license plate is located based on a mapping relation between a ratio of a first horizontal distance to a second horizontal distance, wherein the first horizontal distance is between the center point of the license plate and the left boundary of the head of the vehicle and the second horizontal distance is between the center point of the license plate and the right boundary of the head of the vehicle; and
a calculation module configured to
determine, within the approximate angle range, the tilt angle of the license plate; and
perform a tilt correction on the license plate based on the tilt angle of the license plate.