OBJECT RECOGNITION CONTROL DEVICE AND OBJECT RECOGNITION METHOD

Abstract

This object recognition control device comprises a video data acquisition unit (21) that acquires video data captured by a camera for capturing the area around a vehicle, a travel speed information acquisition unit (24) that acquires information relating to the travel speed of the vehicle, a recognition processing unit (22) that recognizes the video data acquired by the video data acquisition unit (21) as a specific object when a score indicating likeness to a specific object is equal to or greater than a prescribed threshold value, and a presentation processing unit (23) that presents information about the specific object recognized by the recognition processing unit (22) to a driver of the vehicle. The recognition processing unit (23) changes the prescribed threshold value on the basis of the information relating to the travel speed acquired by the travel speed information acquisition unit (24).

Description

[Technical field]

[0001] The present disclosure relates to an object recognition control device and an object recognition method. [Background technology]

[0002] In the case of person recognition in a captured image, when a score indicating human-likeness is equal to or greater than a predetermined threshold as a result of recognition processing using the person recognition processing, it is determined that the person is a person.

[0003] For example, Patent Document 1 discloses a technique for determining that an object is a pedestrian based on a high score indicating pedestrian resemblance in a captured image using a recognition dictionary. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-15029 A Summary of the Invention

[0005] When the vehicle travel speed increases, the edges of people and the like in each frame of the captured image may become unclear, resulting in a decrease in the recognition rate and the possibility of not being able to recognize the presence of people and the like. In addition, if a specific object such as a person cannot be recognized due to the high travel speed, a danger may occur. Furthermore, the driver of the vehicle may become careless about people and the like.

[0006] An object of the present disclosure is to provide an object recognition control device and an object recognition method that are capable of appropriately recognizing a specific object. [Means for solving the problem]

[0007] An object recognition control device according to one embodiment of the present disclosure includes a video data acquisition unit that acquires video data captured by a camera that captures the surroundings of a vehicle, a driving speed information acquisition unit that acquires information regarding the driving speed of the vehicle, a recognition processing unit that recognizes the video data acquired by the video data acquisition unit as a specific object if a score indicating a similarity to a specific object is equal to or greater than a predetermined threshold, and a presentation processing unit that presents information about the specific object recognized by the recognition processing unit to a driver of the vehicle, and the recognition processing unit changes the predetermined threshold based on information regarding the driving speed acquired by the driving speed information acquisition unit.

[0008] An object recognition method performed by an object recognition control device according to one aspect of the present disclosure includes a video data acquisition step of acquiring video data captured by a camera that captures the surroundings of a vehicle, a driving speed information acquisition step of acquiring information regarding the driving speed of the vehicle, a recognition processing step of recognizing the video data acquired in the video data acquisition step as a specific object if a score indicating a similarity to a specific object is equal to or greater than a predetermined threshold, and a presentation processing step of presenting information about the specific object recognized in the recognition processing step to a driver of the vehicle, wherein in the recognition processing step, the predetermined threshold is changed based on information regarding the driving speed acquired in the driving speed information acquisition step.

[0009] According to the present disclosure, a specific object can be appropriately recognized. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an object recognition device according to the first embodiment. [Diagram 2] FIG. 2 is a flowchart showing an example of the flow of the object recognition process according to the first embodiment. [Diagram 3] FIG. 3 is a block diagram illustrating an example of the configuration of an object recognition device according to the second embodiment. [Figure 4]FIG. 4 is a flowchart showing an example of the flow of the object recognition process according to the second embodiment. [Diagram 5] FIG. 5 is a flowchart showing an example of the flow of an object recognition process according to a modified example of the second embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of the flow of the object recognition process according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that the present disclosure is not limited to these embodiments, and when there are multiple embodiments, the present disclosure also includes a configuration in which each embodiment is combined. In the following embodiments, the same parts are denoted by the same reference numerals, and duplicated descriptions are omitted.

[0012] [First embodiment] An object recognition device according to a first embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of an object recognition device according to the first embodiment.

[0013] 1, the object recognition device 1 includes an imaging unit 11, a dictionary data storage unit 12, a display unit 13, a CAN (Controller Area Network) interface unit 14, and a control unit (object recognition control device) 20. The object recognition device 1 may be a device mounted on a vehicle, or may be a portable device that can be used in a vehicle. The object recognition device 1 may also be implemented as a function of a device having a safe driving support function that is installed in advance in a vehicle, a navigation device, a drive recorder, or the like.

[0014] The imaging unit 11 is mounted on the vehicle and arranged to capture an image of the area in front of the vehicle. The imaging unit 11 is, for example, a visible light camera or a far-infrared camera. The imaging unit 11 may be, for example, a combination of a visible light camera and a far-infrared camera. The imaging unit 11 outputs captured image data to the image data acquisition unit 21.

[0015] The dictionary data storage unit 12 stores dictionary data for recognizing various objects from video data. The dictionary data storage unit 12, for example, performs machine learning on various videos of people and stores recognition dictionary data that can verify that an object included in the video data is a person. The dictionary data storage unit 12, for example, performs machine learning on various videos of a bicycle ridden by a person and stores recognition dictionary data that can verify that an object included in the video data is a bicycle ridden by a person. The dictionary data storage unit 12, for example, may perform machine learning on various videos of automobiles and motorcycles and store recognition dictionary data that can verify that an object included in the video data is an automobile, a motorcycle, etc. The dictionary data storage unit 12 is realized by a storage device such as a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory.

[0016] The display unit 13 displays various information. When a specific object is recognized, the display unit 13 notifies the driver of the vehicle by displaying information about the specific object. The display unit 13 is, for example, a display including a liquid crystal display (LCD) or an organic EL (Electro-Luminescence) display.

[0017] The CAN interface unit 14 is an interface that acquires various vehicle information via the CAN. The vehicle information includes status information related to the status of the vehicle. The status information includes, for example, information related to the traveling speed of the vehicle and the acceleration of the vehicle. The vehicle information may include driving operation information related to the driving operation of the vehicle. The driving operation information includes, for example, operation information such as steering operation, braking operation, and accelerator operation.

[0018] The control unit 20 controls the operation of each unit of the object recognition device 1. The control unit 20 is realized, for example, by a central processing unit (CPU) or a micro processing unit (MPU) executing a program stored in a storage unit (not shown) using a RAM or the like as a working area. Thus, the control unit 20 executes an object recognition method by the object recognition device 1. The control unit 20 is also a computer that runs a program according to the present disclosure. The control unit 20 may be realized, for example, by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 20 may be realized by a combination of hardware and software.

[0019] The control unit 20 includes a video data acquisition unit 21, a recognition processing unit 22, a presentation processing unit 23, and a driving speed information acquisition unit 24. In Fig. 1, the video data acquisition unit 21, the recognition processing unit 22, the presentation processing unit 23, and the driving speed information acquisition unit 24 are shown as being connected via a bus BS.

[0020] The video data acquisition unit 21 acquires the video data captured by the imaging unit 11. The video data acquisition unit 21 acquires, for example, video data output by the imaging unit 11, capturing an image of the area ahead of the vehicle.

[0021] The recognition processing unit 22 performs object recognition processing on the video data acquired by the video data acquisition unit 21. The recognition processing unit 22 performs various object recognition processing using, for example, dictionary data stored in the dictionary data storage unit 12, and recognizes objects included in the video data.

[0022] The recognition processing unit 22 uses dictionary data for detecting people to perform person recognition processing on the video data to recognize people included in the video data. The recognition processing unit 22 uses dictionary data for detecting a bicycle ridden by a person to recognize a bicycle ridden by a person included in the video data. In this embodiment, the recognition processing unit 22 recognizes the person included in the video data and the bicycle ridden by the person as a specific object.

[0023] The recognition processing unit 22 recognizes an object contained in the video data as a specific object, for example, when a score indicating the likeliness of the object being a specific object is equal to or greater than a predetermined threshold value as a result of performing object recognition processing on the video data acquired by the video data acquisition unit 21.

[0024] The recognition processing unit 22 calculates a score indicating the likeliness of an object as a person by the person recognition processing, and recognizes the object based on the calculated score. The maximum value of the score indicating the likeliness of an object as a person is, for example, 1.0. For example, under normal circumstances, the recognition processing unit 22 determines that the recognized object is a specific object, that is, a person, when the score is 0.9 or more.

[0025] The recognition processing unit 22 changes the score threshold for determining that a recognized object is a person according to the traveling speed of the vehicle. For example, when the traveling speed of the vehicle is equal to or higher than a predetermined speed, the recognition processing unit 22 lowers the score threshold for determining that a recognized object is a person. For example, when the traveling speed of the vehicle is equal to or higher than 60 km / h, the recognition processing unit 22 determines that a recognized object is a person if the score is equal to or higher than 0.7.

[0026] The recognition processing unit 22 may gradually change the score threshold for determining that the recognized object is a person according to the traveling speed of the vehicle. For example, when the traveling speed of the vehicle is 60 km / h or more and less than 70 km / h, the recognition processing unit 22 may determine that the recognized object is a person if the score is 0.7 or more. For example, when the traveling speed of the vehicle is 70 km / h or more and less than 80 km / h, the recognition processing unit 22 may determine that the recognized object is a person if the score is 0.6 or more. For example, when the traveling speed of the vehicle is 80 km / h or more and less than 90 km / h, the recognition processing unit 22 may determine that the recognized object is a person if the score is 0.5 or more.

[0027] The recognition processing unit 22 may linearly change the score threshold for determining that a recognized object is a person according to the vehicle's traveling speed. For example, the recognition processing unit 22 may change the score to 0.7 when the vehicle's traveling speed becomes 60 km / h, and then linearly decrease the score as the vehicle's traveling speed increases.

[0028] In the present embodiment, the score value by which the recognition processing unit 22 determines that an object is a specific object is merely an example and is not limited thereto. The score value by which the recognition processing unit 22 determines that an object is a specific object may be arbitrarily changed depending on the design.

[0029] In this embodiment, the recognition processing unit 22 recognizes a person and a bicycle on which the person is riding as specific objects, but the specific objects recognized by the recognition processing unit 22 are not limited to these. For example, the recognition processing unit 22 may detect automobiles and motorcycles included in the video data as specific objects.

[0030] The presentation processing unit 23 presents information on the specific object identified by the recognition processing unit 22 to the driver or the like. The presentation processing unit 23 presents information on the specific object to the driver using a display by the display unit 13 or a sound output from a sound output unit (not shown). For this reason, the presentation processing unit 23 functions as a display control unit when presenting information on the specific object to the driver by displaying it on the display unit 13. When displaying information on the specific object on the display unit 13, the presentation processing unit 23 displays the video data acquired by the video data acquisition unit 21 on the display unit 13, and when the presentation processing unit 23 recognizes a specific object, it displays a frame line surrounding the recognized specific object, thereby making the driver recognize the specific object.

[0031] The traveling speed information acquisition unit 24 acquires information related to the traveling speed of the vehicle from the CAN via the CAN interface unit 14, for example.

[0032] [Specific object recognition processing] The specific object recognition process according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of the flow of the specific object recognition process according to the first embodiment.

[0033] With the start of the process in FIG. 2, the control unit 20 starts image capture and person recognition (step S11). Specifically, the control unit 20 causes the imaging unit 11 to start image capture, and the recognition processing unit 22 starts recognition processing on the video data acquired by the video data acquisition unit 21. Then, the process proceeds to step S12. The process in FIG. 2 is started under any condition. For example, this may be when the engine of the vehicle equipped with the object recognition device 1 is started, making the vehicle available for use, or when the operation of the object recognition device 1 is started by a user operation. The process in step S11 may also be executed on the condition that the vehicle is running.

[0034] When the process of step S11 is started, the control unit 20 judges whether or not the traveling speed of the vehicle is equal to or higher than a predetermined speed (step S12). Specifically, the recognition processing unit 22 judges whether or not the traveling speed of the vehicle is equal to or higher than a predetermined speed based on information related to the traveling speed of the vehicle acquired by the traveling speed information acquisition unit 24. If it is judged that the traveling speed is not equal to or higher than the predetermined speed (step S12; No), the process proceeds to step S13. If it is judged that the traveling speed is equal to or higher than the predetermined speed (step S12; Yes), the process proceeds to step S14.

[0035] If the result of the determination in step S12 is No, the control unit 20 determines whether the object is a person using the score threshold for determining that the object is a person as a first threshold (step S13). As a specific example, the recognition processing unit 22 determines that the object is a person when the score indicating the likeliness of the object being a person is 0.9 or more. Then, the process proceeds to step S15.

[0036] If the answer to step S12 is Yes, the control unit 20 determines that the object is a person by setting the score threshold for determining that the object is a person to a second threshold smaller than the first threshold (step S14). As a specific example, the recognition processing unit 22 determines that the object is a person when the score indicating the likeliness of the object being a person is 0.7 or more. Then, the process proceeds to step S15.

[0037] The control unit 20 determines whether or not a person has been detected (step S15). Specifically, the recognition processing unit 22 determines whether or not a person has been recognized based on the score in step S13 or step S14. If it is determined that a person has been detected (step S15; Yes), the process proceeds to step S16. If it is determined that a person has not been detected (step S15; No), the process proceeds to step S17.

[0038] If the result of the determination in step S15 is Yes, the control unit 20 displays a frame around the detected person (step S16). Specifically, the presentation processing unit 23 displays a frame around the detected person so that the driver or the like can recognize the detected person. Then, the process proceeds to step S17.

[0039] The control unit 20 determines whether or not to end the specific object recognition process (step S17). The control unit 20 determines to end the specific object recognition process when, for example, an operation to end the specific object recognition process or an operation to turn off the power supply of the object recognition device 1 is received. If it is determined to end the specific object recognition process (step S17; Yes), the process of Fig. 2 is ended. If it is determined not to end the specific object recognition process (step S17; No), the process proceeds to step S11.

[0040] In the first embodiment, a threshold value of a score indicating the likelihood of a person or a bicycle ridden by a person is changed based on the traveling speed of the vehicle to detect a specific object such as a person or a bicycle ridden by a person included in the video data. In this way, the first embodiment can appropriately detect a person or a bicycle ridden by a person included in the video data even if the traveling speed of the vehicle is faster than a predetermined speed.

[0041] In addition, in the first embodiment, when the vehicle is traveling at a high speed, the recognition process is performed with a low score indicating the specific object resemblance, so that even if a specific object is partially hidden behind an object and would not be recognized as a specific object with a normal score, the specific object is recognized as a specific object, so that the driver of the vehicle can more appropriately know the presence of the specific object.

[0042] When the traveling speed is faster than a predetermined speed, the space below the driver of the vehicle tends to become narrow. In the first embodiment, when the traveling speed of the vehicle is fast, the recognition process is performed by lowering the score indicating the specific object resemblance. Therefore, even objects that are not recognized as specific objects with a normal score are presented as the recognition result, and the driver of the vehicle can drive with a higher priority on safety.

[0043] [Modification of the first embodiment] Next, a modified example of the first embodiment will be described. In the first embodiment, in step S12 of FIG. 2, the control unit 20 determines whether the traveling speed of the vehicle is equal to or higher than a predetermined speed, but the present disclosure is not limited to this. For example, in step S12, the control unit 20 may determine whether the time during which the traveling speed of the vehicle is equal to or higher than the predetermined speed continues for a predetermined time or more. The predetermined time is, for example, 60 seconds, but is not limited to this.

[0044] Therefore, when the state where the traveling speed acquired by the traveling speed information acquisition unit 24 is faster than the predetermined speed continues for a predetermined time or more, the recognition processing unit 22 determines that the person is a person by setting the threshold of the score for determining that the person is a second threshold value smaller than the first threshold value. When the state where the traveling speed is faster than the predetermined speed continues for a predetermined time or more, it does not mean that the traveling speed is temporarily faster than the predetermined speed, but that the traveling speed is continuously faster than the predetermined speed. In such a state, the field of vision of the vehicle driver tends to narrow, so that objects that are not recognized as specific objects with a normal score are presented as the recognition result, and the vehicle driver can drive with a higher priority on safety.

[0045] [Second embodiment] An object recognition device according to the second embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the configuration of the object recognition device according to the second embodiment.

[0046] 3, the object recognition device 1A does not include a CAN interface unit 14, and instead includes a GNSS (Global Navigation Satellite System) receiver unit 15 and a map information storage unit 16, which is different from the object recognition device 1 shown in Fig. 1. The object recognition device 1A also differs from the object recognition device 1 shown in Fig. 1 in that a control unit 20A includes a position information acquisition unit 25 and a map information acquisition unit 26.

[0047] The GNSS receiver 15 receives GNSS signals from GNSS satellites (not shown). The GNSS receiver 15 is realized by a GNSS receiving circuit, a GNSS receiving device, etc. The GNSS receiver 15 outputs the received GNSS signals to the position information acquisition unit 25.

[0048] The map information storage unit 16 stores various types of map information. The map information storage unit 16 is realized, for example, by a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk, or further by an external server connected by a communication function (not shown).

[0049] The position information acquisition unit 25 acquires the GNSS signal from the GNSS receiver 15. The position information acquisition unit 25 calculates, for example, current position information of the vehicle based on the GNSS signal acquired from the GNSS receiver 15 using a known method.

[0050] The map information acquisition unit 26 acquires map information from the map information storage unit 16. The map information acquisition unit 26 acquires, for example, map information from the map information storage unit 16 that corresponds to the current position information calculated by the position information acquisition unit 25.

[0051] The recognition processing unit 22A changes the score threshold for determining that a recognized object is a person according to the possible travel speed of the road on which the vehicle is traveling. For example, when the possible travel speed is equal to or higher than a predetermined speed, the recognition processing unit 22A lowers the score threshold for determining that a recognized object is a person. For example, when the possible travel speed is equal to or higher than 60 km / h, the recognition processing unit 22A determines that a recognized object is a person if the score is equal to or higher than 0.7.

[0052] The traveling speed information acquisition unit 24A determines the possible traveling speed of the road on which the vehicle is traveling, based on the current position information calculated by the position information acquisition unit 25 and the map information acquired by the map information acquisition unit 26. The traveling speed information acquisition unit 24A determines, for example, whether the road on which the vehicle is traveling is a road on which the vehicle is traveling at a speed of 60 km / h or more, such as a motorway or an expressway.

[0053] The traveling speed information acquisition unit 24A may determine the possible traveling speed of the road on which the vehicle is traveling, based on the road signs recognized by the recognition processing unit 22A from the video data acquired by the video data acquisition unit 21. The road signs to be determined include signs for regulating the possible traveling speed depending on the weather, etc. The traveling speed information acquisition unit 24A may also acquire information on the possible traveling speed of the road on which the vehicle is traveling from an external server, etc., using a communication function (not shown).

[0054] [Specific object recognition processing] The specific object recognition process according to the second embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the flow of the specific object recognition process according to the second embodiment.

[0055] The processes of step S21 and steps S23 to S27 shown in FIG. 4 are the same as the processes of step S11 and steps S13 to S17 shown in FIG. 2, respectively, and therefore will not be described.

[0056] The control unit 20A determines whether or not the vehicle is traveling on a road on which the vehicle can travel at a predetermined speed or higher (step S22). Specifically, the traveling speed information acquisition unit 24 determines whether or not the road on which the vehicle is currently traveling is a road on which the vehicle can travel at a predetermined speed or higher, based on the current position information and map information. If it is determined that the road is not a road on which the vehicle can travel at a predetermined speed or higher (step S22; No), the process proceeds to step S23. If it is determined that the road is a road on which the vehicle can travel at a predetermined speed or higher (step S22; Yes), the process proceeds to step S24.

[0057] In the second embodiment, a person or a bicycle ridden by a person included in the video data is detected by changing a threshold value of a score indicating a person-likeness based on the possible travel speed of the road on which the vehicle is traveling. In this way, the second embodiment can appropriately detect a person or a bicycle ridden by a person included in the video data even if the possible travel speed of the road on which the vehicle is traveling is faster than a predetermined speed.

[0058] In addition, in the second embodiment, when the possible travel speed of the road on which the vehicle is traveling is equal to or higher than a predetermined speed, the recognition process is performed with a lower score indicating the specific object resemblance, so that even if a specific object is partially hidden behind an object and would not be recognized as a specific object with a normal score, it is recognized as a specific object. This allows the driver of the vehicle to be more appropriately informed of the presence of the specific object.

[0059] Furthermore, in the second embodiment, when the possible travel speed of the road on which the vehicle is traveling is equal to or higher than a predetermined speed, the recognition process is performed with a lower score indicating the specific object resemblance, so that the number of erroneous recognitions of objects that are not specific objects being determined to be specific objects increases. However, even in the case of erroneous recognition, the presence of a specific object is presented, so that the driver of the vehicle can drive with a higher priority on safety.

[0060] [Modification of the second embodiment] Next, a modified example of the second embodiment will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of the flow of an object recognition process according to the modified example of the second embodiment. The configuration of the object recognition device according to the modified example of the second embodiment is the same as that of the object recognition device 1A shown in Fig. 3, and therefore a description thereof will be omitted.

[0061] The process of step S31 is the same as the process of step S21 shown in FIG. 4, and therefore a description thereof will be omitted.

[0062] After step S31, the control unit 20A determines whether the vehicle has started traveling on a road on which the vehicle can travel at a predetermined speed or higher (step S32). Specifically, the traveling speed information acquisition unit 24 determines whether the vehicle has started traveling on a road on which the vehicle can travel at a predetermined speed or higher based on the current position information and the map information. If it is not determined that the vehicle has started traveling on a road on which the vehicle can travel at a predetermined speed or higher (step S32; No), the process proceeds to step S34. If it is determined that the vehicle has started traveling on a road on which the vehicle can travel at a predetermined speed or higher (step S32; Yes), the process proceeds to step S33.

[0063] If the result of the determination in step S32 is Yes, the control unit 20A determines whether or not the vehicle is scheduled to travel on a road that is travelable at a predetermined speed or higher for a predetermined time or longer (step S33). For example, when starting travel on a national highway or an expressway, the control unit 20A determines that the vehicle is scheduled to travel on a road that is travelable at a predetermined speed or higher for a predetermined time or longer if a route to a destination is set and the planned travel on the road is a predetermined distance or longer. The control unit 20A may add a case where there is no traffic congestion on the road to be traveled to the determination conditions in addition to the case where the planned travel on the road is a processing distance or longer. If it is not determined that the vehicle is scheduled to travel on a road that is travelable at a predetermined distance or higher for a predetermined time or longer (step S33; No), the process proceeds to step S34. If it is determined that the vehicle is scheduled to travel on a road that is travelable at a predetermined distance or higher for a predetermined time or longer (step S33; Yes), the process proceeds to step S35.

[0064] The processes from step S34 to step S38 are the same as the processes from step S23 to step S27, respectively, and therefore will not be described.

[0065] In a modified example of the second embodiment, a person or a specific object such as a bicycle ridden by a person is detected in the video data by lowering the threshold value of the score indicating the likeliness of a person when the vehicle is scheduled to travel for a predetermined time or more on a road where the vehicle can travel at a predetermined speed or more. In such a state, since it is expected that the field of vision of the driver of the vehicle will be narrowed by traveling at a predetermined speed or more on the road where the vehicle has started traveling, objects that would not be recognized as specific objects with a normal score are presented as the recognition result, and the driver of the vehicle can be made to drive with a higher priority on safety.

[0066] [Third embodiment] Next, a third embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the flow of an object recognition process according to the third embodiment. The configuration of the object recognition device according to the third embodiment is the same as that of the object recognition device 1 shown in Fig. 1, and therefore a description thereof will be omitted.

[0067] The process of step S41 is the same as the process of step S11 shown in FIG. 2, and therefore a description thereof will be omitted.

[0068] After step S41, the control unit 20 judges whether the vehicle is in an accelerating state (step S42). Specifically, the traveling speed information acquisition unit 24 judges whether the vehicle is in an accelerating state based on the time transition of the speed information or the acceleration detection result in the traveling direction by an acceleration sensor (not shown). For example, the traveling speed information acquisition unit 24 judges that the vehicle is in an accelerating state when the accelerating state continues for, for example, 5 seconds or more from the start of acceleration. Also, the traveling speed information acquisition unit 24 judges that the vehicle is in an accelerating state when the accelerating state continues for, for example, 5 seconds or more from a state in which the vehicle is traveling at a predetermined speed or more (for example, 60 km / h or more). If it is not judged that the vehicle is in an accelerating state (step S42; No), the process proceeds to step S43. If it is judged that the vehicle is in an accelerating state (step S42; Yes), the process proceeds to step S44.

[0069] The processing from step S43 to step S44 is the same as the processing from step S13 to step S17 shown in FIG. 2, and therefore the description thereof will be omitted.

[0070] In the third embodiment, when the vehicle is in an accelerating state, the threshold value of the score indicating the likelihood of a specific object such as a person or a bicycle ridden by a person is lowered to detect a person or a bicycle ridden by a person included in the video data. As a result, in the third embodiment, since the field of view of the vehicle driver tends to narrow when the vehicle is in an accelerating state, objects that would not be recognized as specific objects with a normal score are presented as the recognition result, allowing the driver of the vehicle to drive with a higher emphasis on safety.

[0071] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the contents of these embodiments. In the embodiments, an example of recognizing a person or a bicycle ridden by a person has been described, but the object to be recognized is not limited to the contents of the embodiments, and the present disclosure can be applied to the recognition of various objects. In addition, the above-mentioned components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-mentioned components can be appropriately combined. Furthermore, various omissions, substitutions, or modifications of the components can be made within the scope of the above-mentioned embodiments. [Industrial Applicability]

[0072] The object recognition control device and the object recognition method of the present embodiment can be used in, for example, a drive recorder. [Explanation of symbols]

[0073] 1,1A Object Recognition Device 11 Imaging unit 12 Dictionary data storage unit 13 Display section 14 CAN interface section 15 GNSS receiver 16 Map information storage unit 20,20A Control section 21 Video data acquisition unit 22, 22A Recognition processing section 23 Presentation processing unit 24, 24A Travel speed information acquisition section 25 Location information acquisition section 26 Map information acquisition section

Claims

1. a video data acquisition unit that acquires video data captured by a camera that captures the surroundings of the vehicle; a travel speed information acquisition unit that acquires information about a maximum travel speed of the road on which the vehicle is traveling based on current position information of the vehicle and map information; a recognition processing unit that recognizes the video data acquired by the video data acquisition unit as the specific object when a score indicating a specific object likeness is equal to or greater than a predetermined threshold; a presentation processing unit that presents information about the specific object recognized by the recognition processing unit to a driver of the vehicle; Equipped with The recognition processing unit changes the predetermined threshold to a lower value when the vehicle is scheduled to travel a predetermined distance or more on a road where the possible travelling speed information acquired by the traveling speed information acquisition unit indicates a possible travelling speed equal to or higher than a predetermined speed. Object recognition control device.

2. The image data acquisition unit acquires image data captured by a camera that captures an image of a scene ahead of the vehicle, The recognition processing unit recognizes a person or a bicycle on which a person is riding as the specific object. The object recognition control device according to claim 1 .

3. A video data acquisition step of acquiring video data captured by a camera that captures the surroundings of the vehicle; a travel speed information acquisition step of acquiring information on a travelable speed of the road on which the vehicle is traveling based on current position information of the vehicle and map information; a recognition processing step of recognizing the image data acquired in the image data acquisition step as a specific object when a score indicating a specific object resemblance is equal to or greater than a predetermined threshold; a presentation processing step of presenting information about the specific object recognized in the recognition processing step to a driver of the vehicle; Including, In the recognition processing step, when the vehicle is to travel a predetermined distance or more on a road on which the possible travelling speed information acquired in the travelling speed information acquisition step indicates a possible travelling speed equal to or higher than a predetermined speed, the predetermined threshold is changed to a lower value. An object recognition method executed by an object recognition control device.

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