WO2019153193A1 - 一种出租车运营监测方法、设备、存储介质和系统 - Google Patents

一种出租车运营监测方法、设备、存储介质和系统 Download PDF

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WO2019153193A1
WO2019153193A1 PCT/CN2018/075840 CN2018075840W WO2019153193A1 WO 2019153193 A1 WO2019153193 A1 WO 2019153193A1 CN 2018075840 W CN2018075840 W CN 2018075840W WO 2019153193 A1 WO2019153193 A1 WO 2019153193A1
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passenger
vehicle
image
image data
feature information
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PCT/CN2018/075840
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English (en)
French (fr)
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廉士国
刘兆祥
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深圳前海达闼云端智能科技有限公司
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Priority to PCT/CN2018/075840 priority Critical patent/WO2019153193A1/zh
Priority to CN201880000144.3A priority patent/CN108369645A/zh
Publication of WO2019153193A1 publication Critical patent/WO2019153193A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification

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  • the invention relates to the field of public transportation operation monitoring, and relates to a taxi operation monitoring method, an electronic device, a storage medium and a system.
  • Taxi is a kind of public transportation mode.
  • the following abnormal conditions may occur in taxi operation: driver-led multi-person carpooling, fugitive offenders (inmates or suspects), passengers' dangerous behaviors (handling/guns and drivers) Or other passengers).
  • These abnormal conditions will cause harm to the society, taxi operators, taxi drivers, etc., and need to be supervised.
  • it mainly relies on manpower to supervise, for example, through urban management to supervise carpooling behavior, and through taxi drivers to identify the legitimacy of passengers, etc., the supervision is inefficient and the effect is poor.
  • the present application provides a taxi operation monitoring method, which can be used for operation supervision of public transportation such as a taxi.
  • a taxi operation monitoring method including: performing feature extraction on acquired in-vehicle image data to obtain passenger image feature information; and displaying the passenger image feature information and the A standard feature information is compared. If the similarity of the comparison result reaches the first preset threshold, an alarm signal is generated.
  • an electronic device comprising: a memory, one or more processors; a memory coupled to the processor via a communication bus; the processor configured to execute the memory
  • An instruction in the storage medium storing instructions for performing the various steps in the method as described above.
  • a computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor to implement the steps of the method as described above.
  • a taxi operation monitoring system comprising: an image acquisition device, configured to perform image data collection on a human body and/or a face of a passenger in the vehicle, and obtain an image of the passenger in the vehicle. Data and image data acquisition time; and, as described above, the electronic device.
  • the technical solution described in the present application compares the image information of the passenger image in the vehicle with the standard feature information to determine whether there is a behavior causing personal injury to the driver during the operation or to carry a suspicious person, thereby real-time operation of the taxi
  • the driver's safety is monitored to improve the safety of taxi operations.
  • the technical solution described in the present application can statistically allocate and manage the overall operation of the taxi based on the statistics of the passenger capacity and the passenger getting on and off information in the taxi operation process.
  • the technical solution described in the present application judges the passenger carpooling situation by the coincidence time of different passengers staying in the car, thereby strengthening the control of the driver-driven multi-person carpooling behavior.
  • FIG. 1 is a schematic diagram of a taxi operation monitoring method according to the present scheme
  • FIG. 2 is a schematic diagram of determining the carpool situation according to the present scheme
  • FIG. 3 is a schematic diagram of an image acquisition area of a passenger in the vehicle according to the present embodiment.
  • the core idea of the solution is to collect image data of the human body and/or face of the passengers in the vehicle in real time through multiple image acquisition devices installed in the taxi, and extract feature data of the image data to obtain passenger image feature information; Image feature information, to determine whether there is a behavior that causes personal injury to the driver, carpooling behavior or suspicious persons to ride the car, thereby strengthening supervision of taxi operations.
  • the present example provides a cloud server-based taxi operation monitoring method, and the steps of the method include:
  • the first standard feature information includes: a human body feature and/or a facial feature that causes a personal injury behavior to the driver.
  • the passenger image feature information includes human body features and/or facial feature information.
  • the image of the in-vehicle can be collected by the image acquisition device, and the feature information of the human body and the face of the passenger is detected from the acquired in-vehicle image data as the passenger image feature information.
  • the feature extraction it can be realized by deep learning-based human target detection, visual-based face detection and the like.
  • face detection can adopt a method based on binary image features combined with multi-classifier cascading
  • human body detection can adopt a method based on human contour or deep learning key point positioning.
  • the human body feature is extracted, the facial features are extracted and formed into a combination feature of the human body feature and the facial feature.
  • the passenger image feature information is compared with the first standard feature information to determine the driver's safety situation or the dangerous situation of carrying the passenger during the taxi operation.
  • the first standard feature information includes: human characteristics and/or facial features causing personal injury behavior to the driver; and the human body characteristic of causing personal injury behavior to the driver may be a dangerous behavior such as a passenger holding a knife, holding a gun or holding other dangerous goods.
  • Human characteristics; facial features can be facial features of dangerous people such as fugitives.
  • the step of generating an alarm signal may be through image classification or target based on deep learning.
  • the method of detecting to achieve comparison and recognition of image feature information of a dangerous behavior such as a knife holding a knife, holding a gun or holding other dangerous goods; or identifying a human body or a human face by a deep learning-based human body or face recognition method
  • the name of the person whether it is a dangerous person in the registered fugitive library.
  • the first preset threshold may be set at 70% to 90%, as long as the similarity is When the degree reaches this threshold, an alarm signal can be sent out, and then the remote staff can confirm the alarm, thereby improving the alarm capability of the system and the safety of the driver.
  • the setting of the preset threshold can be adjusted according to the actual situation, and is not limited to the examples given above.
  • the human body feature, the face feature alone or the human body feature and the face feature may be separately selected according to actual needs, thereby improving the accuracy of the detection. Sex and detection efficiency.
  • the image data in the vehicle needs to contain the image of the passenger, therefore,
  • the image of the passenger in the vehicle is collected by tracking; specifically: the position of the human body and the face detected in the current frame is tracked in the current frame. , including the disappearance from the picture, can be achieved by the method of target tracking. That is, based on the position of the target at the previous moment, the position of the target at the current moment is quickly detected by the continuity and correlation of the target motion between adjacent time points.
  • the image feature information of a certain passenger that appears for the first time in the in-vehicle image data is used as the second standard feature information
  • the above steps are repeated to record the passenger number data within the predetermined operating time and the passenger getting on and off information.
  • the setting of the second preset threshold may also be based on the idea of the fault tolerance rate, and the second preset threshold may be set at 10% to 20%, and the operation of the passenger may be confirmed as long as the similarity is lower than the threshold.
  • the setting of the preset threshold can be adjusted according to the actual situation, and is not limited to the examples given above.
  • the statistics on the number of times of getting on and off can be based on the results of human/face detection and human/face tracking in the previous image, and the stop start and end times of a person on a certain vehicle are counted.
  • different people are denoted as P 1 , P 2 , P 3 , ...; the time of each person i staying in the car is t i0 , t i1 ; in addition, the GPS of each person and every moment can be counted. location information.
  • These statistics are stored in the demographic repository and can be periodically transmitted to the cloud for backup and forensics.
  • the stop start and end time of a certain person on a certain vehicle can be counted, and the carpooling is further performed.
  • the behavior is identified. For two or more people who have time overlap and different time from beginning to end, they can be considered as suspected carpooling. For example, in the above figure, two people P 3 and P 4 have their time coincident and the time points are different (t 30 ⁇ t 40 ⁇ t 31 ⁇ t 41 ), so P 3 and P 4 are likely to be carpooling.
  • the geographical location information provided by the GPS can also be used as a factor for discriminating carpooling. For example, if the above behavior occurs in a subway station, a bus stop, or a train station, the probability of being considered as a carpool is higher.
  • an alarm signal is sent, and the behavior type and image information are sent to the cloud together, and the cloud alarms according to the alarm signal for the taxi company and the police. Used by departmental decision makers.
  • the technical solution described in the present application compares the image information of the passenger image in the vehicle with the standard feature information to determine whether there is a behavior causing personal injury to the driver during the operation or to carry a suspicious person, thereby real-time operation of the taxi
  • the driver's safety is monitored to improve the safety of taxi operations.
  • the technical solution described in the present application can statistically allocate and manage the overall operation of the taxi based on the statistics of the passenger capacity and the passenger getting on and off information in the taxi operation process.
  • the technical solution described in the present application judges the passenger carpooling situation by the coincidence time of different passengers staying in the car, thereby strengthening the control of the driver-driven multi-person carpooling behavior.
  • the present application discloses an electronic device, characterized in that the electronic device comprises: a memory, one or more processors; a memory and a processor connected via a communication bus; the processor being configured to execute instructions in the memory; Instructions for performing the respective steps in the method described in Embodiment 1 are stored in the storage medium.
  • the technical solution described in the present application compares the image information of the passenger image in the vehicle with the standard feature information to determine whether there is a behavior causing personal injury to the driver during the operation or to carry a suspicious person, thereby real-time operation of the taxi
  • the driver's safety is monitored to improve the safety of taxi operations.
  • the technical solution described in the present application can statistically allocate and manage the overall operation of the taxi based on the statistics of the passenger capacity and the passenger getting on and off information in the taxi operation process.
  • the technical solution described in the present application judges the passenger carpooling situation by the coincidence time of different passengers staying in the car, thereby strengthening the control of the driver-driven multi-person carpooling behavior.
  • the present example discloses a computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor to implement the steps of the method as described in embodiment 1.
  • the technical solution described in the present application compares the image information of the passenger image in the vehicle with the standard feature information to determine whether there is a behavior causing personal injury to the driver during the operation or to carry a suspicious person, thereby real-time operation of the taxi
  • the driver's safety is monitored to improve the safety of taxi operations.
  • the technical solution described in the present application can statistically allocate and manage the overall operation of the taxi based on the statistics of the passenger capacity and the passenger getting on and off information in the taxi operation process.
  • the technical solution described in the present application judges the passenger carpooling situation by the coincidence time of different passengers staying in the car, thereby strengthening the control of the driver-driven multi-person carpooling behavior.
  • the present example discloses a cloud server-based taxi operation monitoring system including a plurality of image acquisition devices disposed in a taxi and an electronic device as described in Embodiment 2.
  • the image of the in-vehicle is collected by using multiple image acquisition devices to obtain the acquisition time of the image data and image data in the vehicle; the feature data of the collected image data is extracted by the electronic device, and the extracted passenger image feature information is firstly extracted.
  • the standard feature information is compared, and if the similarity of the comparison result reaches the first preset threshold, an alarm signal is generated.
  • the first standard feature information includes image feature information and/or facial image feature information that causes a personal injury behavior to the driver.
  • the image capturing devices may be respectively disposed at the front ends of the front and rear seats, and the specific setting positions may be adjusted according to the image capturing area. Preferably, it is disposed on the body frame which is obliquely above the middle of the row of seats.
  • the computing unit may use the micro-processing of the built-in identification algorithm and integrate it with other devices in the vehicle or separately in a certain area of the vehicle, and the specific position may be adjusted according to the actual equipment arrangement in the vehicle.
  • the image capturing device can follow the position change of the passengers in the vehicle, and perform image collection on the passengers in the vehicle in a tracking manner, thereby preventing the image capturing device from collecting the image of the passenger in the vehicle.
  • the taxi operation monitoring system is further provided with a storage unit capable of storing acquired image data, all data in the execution process of the computing unit, and first standard feature information.
  • a storage unit capable of storing acquired image data, all data in the execution process of the computing unit, and first standard feature information.
  • the taxi operation monitoring system is further provided with a network module, and the system may acquire the first standard feature information provided by the cloud or other communication information transmitted by the cloud to the system through the network module; or may collect through the network module.
  • the obtained image data and/or all data in the execution of the computing unit are sent to the cloud server.
  • the taxi operation monitoring system is further provided with a positioning module and/or an acceleration detecting module.
  • the positioning module is configured to collect location information of the vehicle in real time, and the electronic device performs real-time positioning of the vehicle based on the location information.
  • the acceleration detecting module is configured to collect acceleration information of the vehicle in real time, and the electronic device determines a running state of the vehicle based on the acceleration information.
  • the positioning module may select a GPS or IMU sensor.
  • the acceleration detecting module may select an acceleration sensor.
  • the taxi operation monitoring system of the present example comprises a camera 2, a sensor, an electronic device as described in Embodiment 2, a storage unit, and a network module.
  • the camera is responsible for taking pictures of the interior of the car.
  • the sensor can use GPS/IMU, etc., which can be used to provide geographic location information.
  • the IMU sensor can also be used to determine the driving state of the taxi 1 (eg, stop, accelerate, decelerate, etc.); the electronic device is used to perform the implementation
  • the method step is as follows: the storage unit is responsible for storing the suspect/prisoner feature database, the person-time statistical information database, and the like; the network module is responsible for communicating with the meta-end to obtain suspect/prisoner feature data, push person statistics, and the like.
  • the corresponding software and hardware modules in the cloud are connected to the in-vehicle device.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

一种出租车运营监测方法，该方法的步骤包括：对采集得到的车内图像数据进行特征提取，获得乘客图像特征信息；将所述乘客图像特征信息与第一标准特征信息进行比对，若比对结果的相似度达到第一预设阈值，则产生报警信号。本方案进一步公开了一种出租车运营监测系统、电子设备和存储介质。本申请所述技术方案通过对车内乘客图像特征信息进行分析，实现对运营过程中的乘客上下车频次统计，并确定是否出现了拼车行为；通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。

Description

一种出租车运营监测方法、设备、存储介质和系统 技术领域
本发明涉及公共交通运营监控领域，涉及一种出租车运营监测方法、电子设备、存储介质和系统。
背景技术
出租车属于一种公共交通方式，出租车运营中可能出现以下异常情况：司机主导的多人拼车、在逃犯人乘车(犯人或嫌疑人乘车)、乘客危险行为(持刀/枪要挟司机或其他乘客)。这些异常情况会给社会、出租车运营方、出租车司机等造成危害，是需要监管的。但当前主要还是依赖人力来监督，例如通过城管来监督拼车行为、通过出租车司机来鉴别乘客的合法性等，监督效率低、效果差。
发明内容
为解决上述技术问题之一，本申请提供了一种出租车运营监测方法，该方法可用于例如出租车等公共交通的运营监管。
根据本申请实施例的第一个方面，提供了一种出租车运营监测方法，包括：对采集得到的车内图像数据进行特征提取，获得乘客图像特征信息；将所述乘客图像特征信息与第一标准特征信息进行比对，若比对结果的相似度达到第一预设阈值，则产生报警信号。
根据本申请实施例的第二个方面，还提供了一种电子设备，所述电子设备包括：存储器，一个或多个处理器；存储器与处理器通过通信总线相连；处理器被配置为执行存储器中的指令；所述存储介质中存储有用于执行如上所述方法中各个步骤的指令。
根据本申请实施例的第三个方面，还提供了一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如上所述方法的步骤。
根据本申请实施例的第四个方面，还提供了一种出租车运营监测系统，包括：图像采集设备，用于对车内乘客的人体和/或面部进行图像数据采集，获得车内乘客图像数据和图像数据采集时间；和，如上所述的电子设备。
本申请所述技术方案通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。
本申请所述技术方案通过对出租车运营过程中的载客量和乘客上下车信息进行统计，能够以此为依据对出租车的整体运营进行合理调配和管理。
本申请所述技术方案通过对不同乘客在车内停留的重合时间对乘客拼车情况进行判断，从而加强对司机主导多人拼车行为的管控。
附图说明
图1为本方案所述出租车运营监测方法的示意图；
图2为本方案所述对拼车情况判断的示意图；
图3为本方案所述车内乘客图像采集区域的示意图。
附图标号
1、摄像头，2、出租车。
具体实施方式
为了使本申请实施例中的技术方案及优点更加清楚明白，以下结合附图对本申请的示例性实施例进行进一步详细的说明，显然，所描述的实施例仅是本申请的一部分实施例，而不是所有实施例的穷举。需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。
本方案的核心思路是通过设置在出租车上的多个图像采集设备实时对车内乘客的人体和/或面部进行图像数据采集，并对图像数据进行特征提取，获得乘客图像特征信息；基于乘客图像特征信息，判断是否出现对司机造成人身伤害的行为、拼车行为或者有可疑人员乘车等情况，从而加强对出租车运营的监管。
实施例1
如图1所示，本实例提供了一种基于云端服务器的出租车运营监测方法，该方法的步骤包括：
对车内图像数据进行采集，获得车内乘客图像数据和图像数据的采集时间；
对采集得到的车内图像数据进行特征提取，获得乘客图像特征信息；
将所述乘客图像特征信息与第一标准特征信息进行比对，若比对结果的相似度达到第一预设阈值，则产生报警信号；
其中，所述第一标准特征信息包括：对司机造成人身伤害行为的人体特征和/或面部特征。所述乘客图像特征信息包括人体特征和/或面部特征信息。
本实例中，可以通过图像采集设备对车内图像进行采集，从采集得到的车内图像数据中检测出乘客的人体、人脸的特征信息作为乘客图像特征信息。对于特征的提取可以采用基于深度学习的人体目标检测、基于视觉的人脸检测等方法实现。例如，人脸检测可以采用基于二进制图像特征结合多分类器级联的方法，人体检测可以采用基于人体轮廓或深度学习关键点定位的方法。本方案中，也可以在人体特征提取时，一并将面部特征提取出来，形成人体特征和面部特征的组合特征。
本实例中，利用乘客图像特征信息与第一标准特征信息进行比对，从而判断出租车运营过程中，司机的安全情况或搭载乘客的危险情况。其中，第一标准特征信息包括：对司机造成人身伤害行为的人体特征和/或面部特征等；对司机造成人身伤害行为的人体特征可以是乘客持刀、持枪或手持其他危险品等危险行为的人体特征；面部特征可以 是在逃犯人等危险人物的面部特征。
本实例中，对于将乘客图像特征信息与第一标准特征信息进行比对，比对结果的相似度达到第一预设阈值，则产生报警信号的步骤，可以通过基于深度学习的图像分类或目标检测的方法来实现对乘客持刀、持枪或手持其他危险品等危险行为的图像特征信息的比对识别；或，通过基于深度学习的人体或人脸识别方法来识别人体或人脸对应的人姓名、是否是已经注册的逃犯库中的某个危险人物。通过比对识别的结果，确定是否产生报警信号；远程设备会实时监控报警信号，若检测到报警信号，则立刻在远程端报警。由于实际图像的特征信息可能与标准特征信息会有一定偏差，因此，为了提高报警能力和对司机的安全性，在设置第一预设阈值时可以将其设置在70％到90％，只要相似度达到这个阈值即可发出报警信号，报警后再通过远程工作人员作进一步确认，从而提高系统的报警能力和对司机的安全性。对于预设阈值的设定可以根据实际情况进行调整，不仅限于上述给出的实例。本方案中，对于乘客图像特征信息与第一标准特征信息的比对，可以根据实际需求选择单独对人体特征、单独对面部特征或同时对人体特征和面部特征进行比对，从而提高检测的准确性和检测效率。
本方案中，为了避免乘客在车内移动导致图像采集设备无法采集到乘客在车内的图像的问题，在车内有乘客时，所述车内图像数据需要包含乘客的图像，因此，当所述乘客位置发生变化时，则采用追踪的方式对车内乘客进行图像采集；具体的：对前一帧画面中检测到的人体、人脸，在当前帧中跟踪此人体、人脸的位置变化，包括从画面 中消失的情况，可以通过目标跟踪的方法来实现。即，基于前一时刻目标所在位置，通过相邻时间点间目标运动的连续性和相关性来快速检测目标在当前时刻的位置。
本实例中，还可以基于车内图像数据和图像数据的采集时间对乘客上下车的次数进行统计，获得载客数量数据和乘客对应的上下车时间信息。具体的，以首次出现在车内图像数据中的某一乘客的图像特征信息作为第二标准特征信息；
按照预定时间间隔，将车内该乘客的图像特征信息与第二标准特征信息进行比对，若比对结果的相似度低于第二预设阈值，则确认完成该乘客的运营，载客数量加1，并记录该乘客对应的上下车信息；
重复上述步骤，记录预定运营时间内的载客数量数据和乘客对应的上下车信息。
其中，对于第二预设阈值的设定也可以基于容错率的思想，可以将第二预设阈值设置在10％到20％，只要相似度低于这个阈值即可确认完成该乘客的运营。对于预设阈值的设定可以根据实际情况进行调整，不仅限于上述给出的实例。
本实例中，对于上下车次数的统计可以基于前面图像中的人体/人脸检测、人体/人脸跟踪的结果，统计某辆车上某人的停留起始和结束时间。其中，不同的人表示为P 1，P 2，P 3，…；每个人i在车中停留的始末时间为t i0、t i1；另外还可以统计出每个人、每个时刻所处的GPS位置信息。这些统计信息会存储在人次统计信息库中，可以周期性地传输给云端用于备份和取证。
本实例中，如图2所示，还可以基于前面图像中的人体/人脸检测、人体/人脸跟踪的结果，统计某辆车上某人的停留起始和结束时间，并进一步对拼车行为进行识别，对于两个以上、有时间重合、始末时间不同的人，可以认为是拼车嫌疑。例如，上图中两个人P 3和P 4，他们的时间有重合、且始末时间点不同(t 30<t 40<t 31<t 41),所以P 3和P 4很可能是拼车的。另外，GPS提供的地理位置信息也可以作为判别拼车的一个因素，例如如果以上行为发生在地铁站、公交站、火车站附近，则判定为拼车的概率更高。
本实例中，当检测到对司机造成人身伤害行为、可疑人物或拼车行为，则发出报警信号，同时将行为类型和图像信息一并发送至云端，云端根据报警信号报警，供出租车公司、警察等部门决策使用。
本申请所述技术方案通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。
本申请所述技术方案通过对出租车运营过程中的载客量和乘客上下车信息进行统计，能够以此为依据对出租车的整体运营进行合理调配和管理。
本申请所述技术方案通过对不同乘客在车内停留的重合时间对乘客拼车情况进行判断，从而加强对司机主导多人拼车行为的管控。
实施例2
本实例公开了一种电子设备，其特征在于，所述电子设备包括： 存储器，一个或多个处理器；存储器与处理器通过通信总线相连；处理器被配置为执行存储器中的指令；所述存储介质中存储有用于执行实施例1中所述方法中各个步骤的指令。
本申请所述技术方案通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。
本申请所述技术方案通过对出租车运营过程中的载客量和乘客上下车信息进行统计，能够以此为依据对出租车的整体运营进行合理调配和管理。
本申请所述技术方案通过对不同乘客在车内停留的重合时间对乘客拼车情况进行判断，从而加强对司机主导多人拼车行为的管控。
实施例3
本实例公开了一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如实施例1中所述方法的步骤。
本申请所述技术方案通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。
本申请所述技术方案通过对出租车运营过程中的载客量和乘客上下车信息进行统计，能够以此为依据对出租车的整体运营进行合理 调配和管理。
本申请所述技术方案通过对不同乘客在车内停留的重合时间对乘客拼车情况进行判断，从而加强对司机主导多人拼车行为的管控。
实施例4
本实例公开了一种基于云端服务器的出租车运营监测系统，该系统包括设置在出租车内的多个图像采集设备和如实施例2所述的电子设备。利用多个图像采集设备对车内图像进行采集，获得车内的图像数据和图像数据的采集时间；利用电子设备对采集得到的图像数据进行特征提取，将提取得到的乘客图像特征信息与第一标准特征信息进行比对，若比对结果的相似度达到第一预设阈值，则产生报警信号。其中，第一标准特征信息包括：对司机造成人身伤害行为的图像特征信息和/或面部图像特征信息。通过将车内乘客图像特征信息与标准特征信息进行比对，确定运营过程中是否出现对司机造成人身伤害的行为或乘运了可疑人员，从而实时对出租车运营过程中司机的安全性进行监测，提高出租车运营安全。本实例中，图像采集设备可以分别设置在前后排座位的前端，具体设置位置可以根据图像采集区域进行调整。优选地，设置在没排座位中间斜上方的车体框架上。本实例中，计算单元可以采用内置识别算法的微处理，并将其与车内其他设备集成或单独设置在车内的某个区域，具体位置可以根据车内实际设备排布情况而调整。
本实例中，所述图像采集设备能够跟随车内乘客的位置变化，以追踪的方式对车内乘客进行图像采集，避免了乘客在车内移动导致图 像采集设备无法采集到乘客在车内的图像的问题。
本实例中，所述出租车运营监测系统进一步设置有存储单元，所述存储单元能够存储采集得到图像数据、计算单元执行过程中的所有数据和第一标准特征信息。当云端需要调取数据时可以随时将运营过程中的所有数据上传至云端。
本实例中，所述出租车运营监测系统进一步设置有网络模块，该系统可以通过网络模块获取云端提供的第一标准特征信息或其他云端传输至该系统的通信信息；也可以通过网络模块将采集得到的图像数据和/或计算单元执行过程中的所有数据发送至云端服务器。
本实例中，所述出租车运营监测系统进一步设置有定位模块和/或加速度检测模块。定位模块用于实时采集车辆的位置信息，所述电子设备基于所述位置信息对车辆进行实时定位。加速度检测模块用于实时采集车辆的加速度信息，所述电子设备基于所述加速度信息确定车辆的行驶状态。本实例中，所述定位模块可以选用GPS或IMU传感器。所述加速度检测模块可以选用加速度传感器。
如图3所示，本实例所述出租车运营监测系统包括摄像头2、传感器、如实施例2所述的电子设备、存储单元和网络模块组成。其中，摄像头负责拍摄车内画面，一般地，考虑到要覆盖出租车1的前排和后排需要两个同步的宽视角摄像头；传感器可以采用GPS/IMU等，其可以用来提供地理位置信息(例如某某汽车站、某某地铁站、某某火车站等)、IMU传感器还可以用来判断出租汽车1的行驶状态(例如停止、加速、减速等)；所述电子设备用于执行实施例1所述方法 步骤；存储单元负责储存嫌疑人/犯人特征库、人次统计信息库等；网络模块负责与元端通信获取嫌疑人/犯人特征数据、推送人次统计数据等。云端有对应的软硬件模块与车载设备对接。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上仅为本发明的实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均包含在申请待批的本发明的权利要求范围之内。

Claims (12)

  1. 一种出租车运营监测方法，其特征在于，该方法的步骤包括：
    对采集得到的车内图像数据进行特征提取，获得乘客图像特征信息；
    将所述乘客图像特征信息与第一标准特征信息进行比对，若比对结果的相似度达到第一预设阈值，则产生报警信号；
    其中，所述第一标准特征信息包括：对司机造成人身伤害行为的人体特征和/或面部特征；所述乘客图像特征信息包括：人体特征和/或面部特征。
  2. 根据权利要求1所述的出租车运营监测方法，其特征在于，所述对采集得到的车内图像数据进行特征提取，获得乘客图像特征信息的步骤之前包括：
    对车内的图像进行采集，获得车内图像数据和图像数据的采集时间。
  3. 根据权利要求2所述的出租车运营监测方法，车内有乘客，其特征在于，所述车内图像数据包含所述乘客的图像，且当所述乘客位置发生变化，则采用追踪采集所述乘客图像。
  4. 根据权利要求3所述的出租车运营监测方法，其特征在于，该方法的步骤进一步包括：
    基于车内图像数据和图像数据的采集时间对乘客上下车的次数进行统计，获得载客数量数据和乘客对应的上下车时间信息。
  5. 根据权利要求4所述的出租车运营监测方法，其特征在于， 所述基于车内图像数据和图像数据的采集时间对乘客上下车的次数进行统计，获得载客数量数据和乘客对应的上下车时间信息的步骤包括：
    以首次出现在车内图像数据中的某一乘客的图像特征信息作为第二标准特征信息；
    按照预定时间间隔，将车内该乘客的图像特征信息与第二标准特征信息进行比对，若比对结果的相似度低于第二预设阈值，则确认完成该乘客的运营，载客数量加1，并记录该乘客对应的上下车信息；
    重复上述步骤，记录预定运营时间内的载客数量数据和乘客对应的上下车时间信息。
  6. 根据权利要求5所述的出租车运营监测方法，其特征在于，所述基于车内图像数据和图像数据的采集时间对乘客上下车的次数进行统计，获得载客数量数据和乘客对应的上下车时间信息的步骤还包括：
    基于车内图像数据和图像数据的采集时间，比对不同乘客在车内停留的重合时间，若重合时间小于预定时间，则产生拼车报警信号。
  7. 根据权利要求1所述的出租车运营监测方法，其特征在于，该方法的步骤进一步包括：
    对当前车辆的位置信息和/或运行状态信息进行采集。
  8. 一种电子设备，其特征在于，所述电子设备包括：存储器，一个或多个处理器；存储器与处理器通过通信总线相连；处理器被配置为执行存储器中的指令；所述存储介质中存储有用于执行权利要求 1至7任意一项所述方法中各个步骤的指令。
  9. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现权利要求1至7中任意一项所述方法的步骤。
  10. 一种出租车运营监测系统，其特征在于，该系统包括：
    图像采集设备，用于对车内图像进行采集，获得车内的图像数据和图像数据的采集时间；
    如权利要求8所述的电子设备。
  11. 根据权利要求10所述的出租车运营监测系统，其特征在于，所述图像采集设备能够跟随车内乘客的位置变化，以追踪的方式对车内乘客进行图像采集。
  12. 根据权利要求10或11所述的出租车运营监测系统，其特征在于，该系统进一步包括：
    定位模块，用于实时采集车辆的位置信息，所述电子设备基于所述位置信息对车辆进行实时定位；和/或，
    加速度检测模块，用于实时采集车辆的加速度信息，所述电子设备基于所述加速度信息确定车辆的行驶状态。
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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111178231A (zh) * 2019-12-26 2020-05-19 航天信息股份有限公司 一种对象监测方法及装置
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US11893793B2 (en) * 2018-03-28 2024-02-06 Gal Zuckerman Facilitating service actions using random imagery data captured by a plurality of on-road vehicles
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Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110958566B (zh) * 2018-09-26 2021-05-28 上海飞田通信股份有限公司 定位识别接载乘员系统及其控制方法
CN111079474A (zh) * 2018-10-19 2020-04-28 上海商汤智能科技有限公司 乘客状态分析方法和装置、车辆、电子设备、存储介质
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CN110197150B (zh) * 2019-05-27 2023-06-27 大陆投资(中国)有限公司 出行服务方法、装置及系统
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CN111738158A (zh) * 2020-06-23 2020-10-02 上海商汤临港智能科技有限公司 交通工具的控制方法及装置、电子设备和存储介质
CN112291677A (zh) * 2020-08-13 2021-01-29 惠州市德赛西威汽车电子股份有限公司 一种自适应音场调节方法、系统及存储介质
CN112906515A (zh) * 2021-02-03 2021-06-04 珠海研果科技有限公司 车内异常行为识别方法、系统、电子设备及存储介质

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105292052A (zh) * 2015-11-12 2016-02-03 福州大学 车辆行车安全自动监控与报警系统
CN106599813A (zh) * 2016-12-05 2017-04-26 中兴智能视觉大数据技术（湖北）有限公司 一种基于人脸识别的铁路安检安防管理系统

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104217537A (zh) * 2014-09-01 2014-12-17 南通北城科技创业管理有限公司 一种出租车安全系统
CN104243927A (zh) * 2014-09-27 2014-12-24 江阴延利汽车饰件股份有限公司 自动识别嫌疑人的安保机器人控制平台
CN104217539B (zh) * 2014-09-28 2016-07-06 江阴延利汽车饰件股份有限公司 出租车人员实时监控平台
CN106170826B (zh) * 2016-06-03 2018-10-09 深圳市锐明技术股份有限公司 出租车乘客人数的监控方法及系统
CN106709470A (zh) * 2017-01-04 2017-05-24 西南交通大学 基于人脸识别的列车在途检票方法
CN206363345U (zh) * 2017-01-13 2017-07-28 山西正方人和科技有限公司 一种车载声纹和人脸识别设备
CN107085720A (zh) * 2017-06-06 2017-08-22 信利光电股份有限公司 一种车载报警系统及方法
CN107240289A (zh) * 2017-07-24 2017-10-10 济南博图信息技术有限公司 一种公交车线路优化管理方法及系统

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105292052A (zh) * 2015-11-12 2016-02-03 福州大学 车辆行车安全自动监控与报警系统
CN106599813A (zh) * 2016-12-05 2017-04-26 中兴智能视觉大数据技术（湖北）有限公司 一种基于人脸识别的铁路安检安防管理系统

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11893793B2 (en) * 2018-03-28 2024-02-06 Gal Zuckerman Facilitating service actions using random imagery data captured by a plurality of on-road vehicles
CN111178231B (zh) * 2019-12-26 2023-07-18 航天信息股份有限公司 一种对象监测方法及装置
CN111178231A (zh) * 2019-12-26 2020-05-19 航天信息股份有限公司 一种对象监测方法及装置
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CN112929827A (zh) * 2021-01-20 2021-06-08 深圳市航通北斗信息技术有限公司 建筑废弃物的排放监控方法、装置及介质
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CN113592599A (zh) * 2021-08-02 2021-11-02 湖南蓝眼科技发展有限公司 基于定位信息的出租车拼车方法、电子设备及可读存储介质
CN113592599B (zh) * 2021-08-02 2024-05-31 湖南蓝眼科技发展有限公司 基于定位信息的出租车拼车方法、电子设备及可读存储介质
CN114463933A (zh) * 2022-01-24 2022-05-10 重庆数宜信信用管理有限公司 一种基于人脸识别的监控系统及方法
CN116166710A (zh) * 2022-11-17 2023-05-26 北京白龙马云行科技有限公司 时长处理方法、装置、电子设备和存储介质
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CN116166710B (zh) * 2022-11-17 2024-01-12 北京白龙马云行科技有限公司 时长处理方法、装置、电子设备和存储介质

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