COMPUTER-IMPLEMENTED METHOD FOR THE AUTOMATIC DETECTION OF ILLEGAL WASTE

Abstract

COMPUTER-IMPLEMENTED METHOD FOR THE AUTOMATIC DETECTION OF LITTER LOGGING A computer-implemented method for detecting video sequences of interest in a monitored area, comprising: Acquisition of a first video stream by a camera (10); Extraction of a first series of images from the first video stream defining a second acquired image stream; Detection of a change specific to a deposit (20) within the second image stream; Extraction by a component of the camera (10) of a second series of images from the first acquired video stream at a second sampling rate; Detection of the presence of a human being (U1) within at least one image of the second image series; Transmission of an extract from the first video stream containing the first and second dates to a second remote server. Figure for the abstract: Fig. 1

Description

Title of the invention: COMPUTER-IMPLEMENTED METHOD FOR THE AUTOMATIC DETECTION OF WILD WASTE Field of invention

[0001] The invention relates to the field of methods for detecting objects that have been abandoned by a human in an image stream. The field of the invention relates more particularly to the detection of fly-tipping in areas prone to fly-tipping. State of the art

[0002] There are video surveillance solutions for detecting abandoned packages or objects dumped illegally in areas dedicated to the collection and sorting of waste. However, these solutions are costly in terms of computing time and volume of data processed due to the flow of acquired images to be analyzed. Furthermore, these solutions are faced with the problem of keeping part of the information collected anonymous, including throughout the detection phase.

[0003] Application CN107133974 published on 2017-06-02 discloses a method for detecting this type of object to characterize changes in the image and in particular to detect the presence of vehicle(s) by categorizing the type of vehicle. This method implements machine learning algorithms to best classify the reason for the presence of vehicles near a road or on a road to link them to specific acts. The solution focuses on processing the recognition of the background of the image, in particular to process differences in brightness. A first problem is that the changes detected do not use the data on the duration of this change to qualify it as a detection of waste deposit(s). A second drawback of the method is to process large volumes of data. Indeed, this prior art solution must perform numerous processing operations before detecting the portions of videos of interest.These treatments are expensive and require local electronic equipment requiring large computing resources.

[0004] Application US11048942 published on 2018-05-08 directly addresses the problem of the difficulty of classifying heterogeneous fly-tipping and false detections. To this end, this document describes a solution for detecting changes in the image and detecting activity. A first problem is that the detected changes do not exploit the data on the duration of this change to qualify it as a waste deposit(s). In addition, a second problem is that the This solution also requires large local computing capacities and does not allow for rapid detection of changes while maintaining a processing method ensuring the anonymity of certain information collected in the image.

[0005] There is a need to propose a solution that is inexpensive in terms of computing time and volume of data processed while ensuring anonymity of the data processed when detecting an act characterizing fly-tipping. Summary of the invention

[0006] According to a first aspect, the invention relates to a computer-implemented method for detecting video sequences of interest from an area under surveillance comprising: • Acquisition of a first video stream by at least one camera arranged to capture images of an area of ​​interest; • Recording the first video stream in a memory, said recordings corresponding to recorded videos of a predefined duration; • First extraction of a first series of images from the first video stream defining a second stream acquired according to a first sampling frequency by a first computer of a first device; • Transmission of the second image stream to a second computer; • Detection of a deposit by implementing a first algorithm for detecting changes in the images applied to the second image stream, said first algorithm being configured to determine a first position of a new element in said image persisting for a minimum duration in at least one image of the second image stream; • Transmission to the first equipment of first detection data relating to the presence of a first element in the image of the second image stream, of an identifier of a first image associated with a first date, said first image comprising the first detected element; • Second extraction by a component of the first equipment of a second series of images from the first video stream acquired according to a second sampling frequency over a first time window comprising the first date, the second series of images defining a third video stream; • Transmission to the second computer or to a third computer of the second series of images; • Detection of the presence of a human being within at least one image of the second series of images by implementing a first learning function in a predefined area around the first position of each processed image of the second series of images; • Transmission to the first equipment of a second detection data relating to the presence of a human being in at least one image of the second series of images, of an identifier of a second image associated with a second date, said second image comprising a detected presence of a human being; • Transmission of an extract from the first video stream, a second time window corresponding to the duration of the extract including the first date and the second date to a second remote server.

[0007] One advantage is that it is possible to detect activities characteristic of an illegal dumping of waste while analyzing only a small portion of data, which allows only a small portion of local resources to be mobilized. This allows in particular to increase the autonomy of the equipment installed locally.

[0008] According to one embodiment, the deposit corresponds to a deposit of waste such as household waste, rubble or furniture, etc. According to one embodiment, the method of the invention comprises a step aimed at automatically recognizing the type of deposit, i.e. the type of object deposited. For this purpose, a machine learning algorithm can be used to classify the type of deposit according to the nature of the object which is present in the image. This algorithm can be trained from a training data set. A CNN type network, designated in the English literature as a "convolutional neural network" and designating a convolutional neural network can be implemented.

[0009] According to one embodiment, the method comprises: • Blurring of at least a portion of each image of the first series of images by implementing a blurring function, said blurring being carried out by the second computer after transmission of the second stream of images to said second computer; • Blurring of at least one image portion of each image of the second series of images by implementing a blurring function, said blurring being carried out by the second or third computer after transmission of the second image stream to said second computer.

[0010] One advantage is to ensure anonymity of the information transmitted by the camera which performs the image capture during the transmission of the emitted images to a resource of a data network.

[0011] According to one embodiment, the second computer is a computer of a remote server, said remote server comprising a first blurring function for automatically blurring a second area of ​​interest of each image received from the stream of received images, said first software function comprising the implementation of a machine learning model configured to detect and classify the plates registration and faces, said configuration comprising a machine learning model learned from a training data set, said first software function further comprising an algorithm for blurring said second detected area of ​​interest.

[0012] One advantage is to automatically detect personal information to produce anonymized images.

[0013] According to one embodiment, the detection of a deposit comprises: • A detection of a change between at least two consecutive images of the second image stream, said change characterizing a first element present in the image; • A detection of the presence of the first element within a plurality of images of the second image stream, said images being considered previously and / or successively to the image within which a change was detected; • An extraction of the position of said element within the image.

[0014] One advantage is to detect a lasting change within a stream of images.

[0015] According to one embodiment, the detection of the presence of the first element within a plurality of images of the second image stream is carried out by performing a first average of pixel values ​​considered in each image of a first subset of images preceding the image within which a change was detected and by performing a second average of pixel values ​​considered in each image of a second subset of images following the image within which a change was detected, the difference between the first and the second average making it possible to deduce the presence of a deposit.

[0016] One advantage is to locate the position of a change in the image.

[0017] According to one embodiment, the detection of a deposit comprises at least one comparison of characteristic properties of a grouping of pixels between at least two images of the first series of images.

[0018] According to one embodiment, upon detection of a first element, the first calculator comprises: • A generation of a first detection data relating to the presence of a first element in the image, • An identification of the first image comprising the first element detected in the image either by an image identifier or by a date described by a time code in a predefined time window; • the generation of at least a first position of said first element in the image.

[0019] According to one embodiment, the first date corresponds to a time code of a image in the first image stream, that is, to that of an image sampled at the first frequency.

[0020] One advantage is to use metadata to quickly extract images of interest in a video stream.

[0021] According to one embodiment, the second sampling frequency is strictly greater than the first sampling frequency, said first time window comprising a portion of the image stream preceding the first date.

[0022] According to one embodiment, the detection of the deposit comprises the identification of a time interval between two dates between which the deposit is detected and in that the detection of the presence of a human being comprises the identification of a time interval between two dates between which the presence of a human being is detected.

[0023] According to one embodiment, the first time window of the second extraction is defined by the two dates of the time interval identified during the detection of the deposit.

[0024] According to one embodiment, the second time window of the second extraction is defined by the two dates of the time interval identified during the detection of the deposit.

[0025] According to one embodiment, detecting a human presence comprises detecting an activity characteristic of a human being.

[0026] According to various examples, the characteristic activity comprises the detection of a posture, a movement and / or a set formed by a human being and an object. A machine learning algorithm can be used to recognize a shape, a movement or a posture automatically. Training from a set of labeled images can be used. A CNN-type neural network can be implemented for this purpose, the acronym for which in the English literature designates “convolutional neural network” and designates a convolutional neural network.

[0027] According to one embodiment, the detection of a characteristic activity comprises the implementation of a first learning function receiving as input images from the first video stream and implementing a machine learning model learned from a set of training data making it possible to classify images of an individual and to classify movements of said individuals in a given portion of each processed image.

[0028] According to one embodiment, the second date corresponds to a date of an image sampled and selected in a second time window corresponding to the period during which a characteristic activity was detected.

[0029] According to one embodiment, the second transmission also comprises the transmitting at least a first position of said first detected element in the image and in that the fourth transmission also comprises transmitting at least a second position of said detected activity in the identified image.

[0030] According to one embodiment, the first transmission and the transmission are carried out to the same computer, the second computer and the third computer being in this case the same computers.

[0031] According to another aspect, the invention relates to equipment intended to be fixed to a mast, said equipment comprising a camera for acquiring images of an area of ​​interest and at least a first computer and a memory for implementing the steps of the method of the invention.

[0032] According to another aspect, the invention relates to a system comprising equipment comprising a camera for acquiring images of an area of ​​interest and at least a first computer and a memory, said system further comprising a remote server for implementing the steps of the method of the invention. Brief description of the figures

[0033] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0034] [Fig-1]: a representation of an area in which an embodiment of the system is installed at the top of a mast for the observation of said area of ​​interest;

[0035] [Fig.2]: an example of a system of the invention comprising a plurality of equipment for implementing the method of the invention;

[0036] [Fig.3]: an example of implementation of steps of the method of the invention comprising for the detection of an illegal deposit;

[0037] [Fig.4]: an example of equipment comprising a camera and calculation means for extracting images to be analyzed;

[0038] [Fig.5]: an example of representation of time windows and dates of detection of waste deposit(s) and of an activity characteristic of human activity,

[0039] [Fig.6]: an example of illustration of the data exchanges between the different equipment of the system of the invention. Description of the invention

[0040] [Fig.l] represents a scene in which containers 5 are present for receiving household waste, glass or cardboard. The scene represented corresponds to an area of ​​interest Zi in which it is desired to set up optical detection of an activity of illegal dumping of waste or objects by an individual Up

[0041] The method and system of the invention relate to any area of ​​interest having containers or not likely to be the subject of automatic monitoring of the made of illegal dumping. These areas may correspond to storage locations, waste disposal sites, drop-off areas, etc.

[0042] The monitoring of this area of ​​interest Zi is carried out by at least one camera 10 installed at the top of a mast. According to different embodiments, different cameras can be used and installed either in a co-located manner at an attachment point or at different attachment points of the scene. Any other installation of the camera or cameras on an element other than a mast can be carried out. Typically, the camera can be installed at the top of a roof or a chimney of a neighboring building, an electrical or hydraulic installation or even a natural element such as a tree. The camera is configured to cover, according to a viewing angle, an area of ​​interest Zi that one wishes to monitor. When different cameras are installed at different installation points, they can be configured so as to cover a wider area or to obtain different viewing angles of the same scene.

[0043] The area of ​​interest Zi here represents an individual Ui depositing one or more illegal dumps 20 next to the containers 5. In this case, the individual Ui has parked his car 15 nearby. When he leaves the area of ​​interest Zi, this latter scene has been modified from the point of view of the image captured by the camera 10 since a new object 20 is present in the image. This object 20 most often persists for a few hours, or even a few days in the same place before being removed by a collection service. The change made in the image is called a deposit insofar as from a given date, a new object 20 in the scene will persist for a certain time in the images acquired after its deposit. The invention makes it possible in particular to detect and classify this specific change.

[0044] When the individual Ui arrives to drop off an object 20, different actions follow one another: the individual parks his car 15, generally opens the trunk of the vehicle, extracts the objects 20 that he wishes to get rid of, then a phase of moving these objects 20 takes place to a given location. Then, the individual Ui goes back to his car and sets off at the wheel of his vehicle 15 which leaves the scene.

[0045] Other sequences of actions may take place in the case of fly-tipping. For example, the car may be a truck or another vehicle, the individual may be accompanied by other individuals. The objects 20 transported may include different sizes and be of different natures and finally may be more or less voluminous and include more or fewer elements. It may be that the individual chooses another place to park and another place to deposit the objects.

[0046] The set of actions carried out at the time of deposit is called a characteristic activity ACTi of a human being during the illegal deposit of any object 20.

[0047] In a first embodiment, the characteristic activity ACTi simply corresponds to the presence of at least one human in a given area. The area may match the whole image or a portion of the image.

[0048] In a more elaborate embodiment, the characteristic activity ACTi corresponds not only to the presence of a human in a given area but also to an additional pattern such as a posture, a shape and / or a movement or a combination of one of these data.

[0049] The posture may correspond to a position of the body of a person who is leaning over for example, that is to say probably in the process of putting down an object.

[0050] The movement may correspond to a "back and forth" from a first position to a second position meaning that a human being moves for example from a car to an area of ​​space and returns to his car. Another movement may correspond to a movement in which a human being bends down and gets up.

[0051] The shape may correspond to a set formed by a body and an object carried by the human being. This shape may be characterized by a sample of training data representing different shapes corresponding to human beings carrying an object. These different shapes may correspond to silhouettes of human beings carrying bags, waste, objects, etc.

[0052] In order to recognize a characteristic activity, a machine learning algorithm can be implemented from a set of training data and for example from a neural network such as a CNN for posture or shape detection, or even from an RNN or a CNN for analyzing a movement on a plurality of images.

[0053] In the remainder of the description, we will speak of characteristic activity ACTi to characterize at least the presence of a human being and possibly according to one embodiment we will speak of characteristic activity ACTi to characterize the presence of a human being and of additional data characterizing an activity of a human being such as a posture, a movement or a complex shape.

[0054] According to one embodiment, the characteristic activity can characterize the presence of two or three human beings in the image and not be limited to the presence of a single person.

[0055] In the context of the invention, it can be considered that each phase of an illegal deposit is itself a characteristic ACTh activity. The invention makes it possible in particular to detect and classify this characteristic activity. ACTh In the remainder of the description, a characteristic ACTi activity will relate either to all of the phases, or to one of the phases in particular, or to a combination of phases.

[0056] [Fig. 2] represents a system of the invention in which a piece of equipment 10 is installed at the top of a mast. This piece of equipment 10 can be associated with a first server SERVi which carries out part of the processing of the method of the invention. This server SERVi can be configured to receive images transmitted from the piece of equipment 10. A The advantage of this configuration is that it allows remote operations to be carried out so as to lighten the load on the equipment 10, in particular to promote its autonomy. The equipment 10 can be configured, for example, to acquire images and record them and carry out some operations aimed at transmitting images remotely and possibly receiving instructions aimed at initiating automatic actions such as sending notifications, sending portions of video, sending indicators or images, etc. In this configuration, the equipment 10 consumes few resources and can operate autonomously for longer periods.

[0057] According to an alternative, the equipment 10 carries out all the image processing carried out on the acquired videos to execute the method of the invention. In the latter case, the equipment 10 comprises means such as electronic cards, chips, microprocessors, computers, to implement all the steps of the method. This configuration is advantageous when a power supply is available on the monitoring site or when it is desired to avoid transmitting information via a data network such as the Internet or when it is not desired to implement a blurring operation and more generally anonymization on a video stream transmitted from the equipment 10 to a remote server SERVi.

[0058] According to another alternative, different equipment is installed on site, including equipment 10. One advantage is to distribute the calculation loads, to segment the different functions performed, to facilitate maintenance operations or to access electrical resources of a network.

[0059] [Fig.2] further represents a second server SERV2 connected to the data network NET so that either the equipment 10 or the first server SERVi can address data to this server SERV2.

[0060] The method of the invention aims to be executed by one or more centralized or distributed computers with the objective of extracting, where appropriate, a portion of an acquired video comprising a detection of an illegal dump in order to transmit it to a second remote server SERV2. This second server SERV2 may be that of a third-party system providing a given service. For example, the third party may be a departmental, municipal, or regional service aiming to use these videos to raise awareness among a population, list types of illegal dumping, profile individuals illegally dumping objects, identify individuals illegally dumping objects, or count illegal dumping in order to quantify an effort, an investment, or a cost for an organization. According to another example, the third party may be a service such as that of the police or the gendarmerie in order to automatically generate fines.

[0061] [Fig.2] also illustrates an operating console denoted CONSi allowing an operator or an operator to exploit the portions of videos produced and transferred automatically by the method of the invention.

[0062] According to one embodiment, the system of the invention does not include the CONSi operating console, nor the second SERV2 server.

[0063] According to the embodiment variants, the system of the invention comprises only the equipment 10. According to a second embodiment variant, the method of the invention comprises the equipment 10 and a remote server SERVi. According to another embodiment, the equipment 10 can be replaced by two pieces of equipment on the same site distributed between a first image acquisition equipment and a local server powered by an available electrical supply.

[0064] [Fig. 3] represents a step diagram of an embodiment of the method of the invention. This diagram represents the steps carried out by the first computer Ki of the first equipment 10, by the second computer K2 which can be, depending on the circumstances, in the first equipment 10, in a secondary equipment comprising a dedicated computer K2 and positioned on the site of the first equipment 10 or within a remote server SERVi.

[0065] The first step ACQi consists of acquiring a stream of images FLi from at least one camera Ci. According to one embodiment, the method can take into account two streams of images FLi, FLi' acquired by at least two cameras. The images can be 2D or 3D images depending on the type of camera used. When a camera comprising two lenses is installed, a 3D image can be acquired.

[0066] The acquired images may be images obtained from a wide-angle camera or a camera with a given depth of field. According to one embodiment, two cameras combining different optical properties make it possible to enrich the acquired video stream. According to an exemplary embodiment, an infrared image stream may be added to the first stream, said stream being acquired by means of an infrared sensor. One advantage is to increase the capacity for analyzing night images or images with low light.

[0067] According to one embodiment, the camera Ci may comprise a software component configured to detect changes in the image, such as passages of cars, pedestrians, changes in brightness, etc. When such a camera is used, a first annotation of the images of the first video stream FLi may be carried out. This annotation may then be used to corroborate detected deposit labels OBi or characteristic activities ACTi. This annotation may be used to rule out false positives.

[0068] The method of the invention comprises a step which aims to record the images acquired from the video stream FLi. The recording can be carried out for example by portions of video sequences over predefined durations. The recordings can be indexed by date so that video segments can be identified quickly from timecodes of an image subsequently detected by the process.

[0069] The method of the invention advantageously comprises a step of extracting a series of images Si from the first acquired flow FLi.

[0070] According to a first embodiment, the series of images is extracted from the video stream FLi upon each detection of a characteristic activity ACTi and / or upon each detection of a human presence ACT i by the camera.

[0071] According to another embodiment, which can be combined with the latter, the series of images Si is extracted from a sampling at a frequency fei of said first stream FLi. One advantage is to extract, for example, a stream of one image per hour, or one image per second or an intermediate value of the number of images over a given duration, for example a few minutes. According to one example, one image per hour or one image per minute can be extracted from the first video stream FLi. One advantage is to generate a series of images Si which is light in weight and which makes it possible to implement an algorithm that requires little computing resources to perform the detection of an OBi deposit.

[0072] The extracted series of images Si is then possibly recorded locally within a memory of the equipment 10. The method of the invention comprises a step TRi of transmitting the first series of images Si to a computer K2 or SERVi or possibly Ki in charge of detecting the deposit OBi. When the images are transmitted to a server SERVi, parameter data is used to automatically send the series of images Si to the server. The parameter data may comprise an address of a resource locator such as a URL or an FTP address or any other address allowing access to a resource connected to a data network NET, such as the Internet. In addition, the parameter data may comprise authentication data of a service comprising an identifier and a password or other data allowing secure authentication to be ensured with a remote resource.A third server (not shown) can be implemented to provide the authentication service and grant rights to access a resource of the SERVi server, and possibly SERV2.

[0073] For this purpose, a communication interface INTc can be configured to encode the images of the first series of images Si and transmit the data to network equipment such as a switch or a router making it possible to transmit the data on the data network NET. According to one embodiment, the communication interface INTc can comprise means for directly transmitting data frames on the network, for example via a 4G network.

[0074] According to another embodiment, the INTc interface is configured to transmit the data via a wireless communication channel to another equipment (not shown) located on the same site which includes analysis means, such as a server. One advantage is to use power supply resources of the second equipment to implement algorithms for detecting deposits OBi and / or detecting characteristic activity ACTb, in particular when the first equipment 10 operates on battery power. In one embodiment, the first equipment 10 is connected to a power supply system.

[0075] The method of the invention further comprises a step of DETi detection of OBi deposits. This step can be carried out according to the configurations by any hardware resource allowing calculations to be carried out, in particular for carrying out image analyses.

[0076] The step of detecting DETi the deposits advantageously comprises an algorithm aimed at comparing the images of the second series Si with each other. A method of analyzing the new deposits between the different images of the first series Si can be implemented. According to an exemplary embodiment, pixel properties are analyzed to be compared. These properties can include any characteristic property of pixels such as their color, their intensity, their gray level, their brightness, their saturation, their hue, etc. According to one example, properties relating to groups of pixels of the same portion of an image can be compared between two successive images of the series of images or between images spaced several images apart in the series of images.The properties of a group of pixels gathered in the same area may include properties obtained by performing any mathematical operation such as means, medians, calculations of distribution of certain properties or other mathematical operation. In the latter case, the comparisons are relative to means, medians or properties of distributions of certain properties in the grouping of pixels.

[0077] This analysis makes it possible to detect a new element 20 in the image. This analysis comprises a step of comparing images with each other from the first series of images Si, for example by subtracting them to produce a differential image. According to one example, a step of eliminating low frequencies in the frequency domain of the differential image is carried out so as to isolate singular changes in an area of ​​the image. This step makes it possible in particular to avoid homogeneous changes between two images linked for example to a drop in brightness. The changes are identified in the differential image to detect a change in the image. According to one embodiment, an algorithm for detecting edges and corners makes it possible to determine the shape of the new object detected in the image in order to characterize properties of the change. This characterization of the properties of the new object and therefore of the change makes it possible to classify the type of change detected.

[0078] Finally, an analysis of the qualification of the change over time is carried out so as to label the OBi deposits. This step aims to detect a lasting change on a subset of images of the first set of images Sp. In order to carry out this step, a plurality of images following the detection is considered to measure whether the change is still present over time. This step makes it possible in particular to rule out all rapid changes linked to furtive passages of vehicles or individuals or any other change which is not lasting within the first series of images Sp.

[0079] When a deposit OBi is detected, marking data is generated so as to identify the image IMi and the date ti associated with this image of the first series of images Sp. According to an exemplary embodiment, data characterizing the shape of the object and / or the region of the image IMi in which the change was made can be generated.

[0080] According to one embodiment, a learning function, called second learning function FA2, can be implemented by the method of the invention, for example in combination with the implementation of a previously described algorithm. For this purpose, a machine learning model comprising a parameterization such as a series of coefficients can be learned by means of gradient descent to train a regression model. One interest is to validate the deposits in the OBp image

[0081] According to one example, image sequences with vehicle passages not characteristic of a depot are classified as non-depot activities. According to another example, a change in brightness of an image sequence is classified as non-depot activities. Conversely, sequences comprising an object from an image of the sequence near an area of ​​interest and which remains for a given period of time can be associated with an OB depot detection. i-

[0082] The method of the invention then comprises a step aimed at transmitting a notification TR2 to the first equipment 10 making it possible to carry out a second extraction of images S2 in a predefined time window Di. The notification advantageously comprises data making it possible to characterize the images of interest, time codes, possibly image identifiers, geometric zones of the image delimiting a contour linked to the detected change or even a region of interest of the image IMi and service authentication parameters making it possible to secure the transmissions between the equipment.

[0083] According to an exemplary embodiment, the positions in the image of the OBi deposits are not transmitted to the first Kb calculator. In this case, the positions in the image and possibly the geographical areas or the groupings of pixels associated with the OBi deposits are recorded in a memory of the equipment containing the K2 calculator or in a memory of the first SERVi server. One advantage is to reduce data transfers between the different equipment in the system. One advantage is to reuse these position or zone data in the detection of the characteristic ACTi activity carried out in a second step from the second series of S2 images.

[0084] The second series of images S2 can also be considered as a video stream, called third video stream FL3. It is understood that the third video stream has a sampling frequency of the images of the first video stream FLi greater than the sampling frequency of the images of the first video stream FLi having generated the second video stream FL2. The time windows of the transmitted video streams FL2 and FL3 can be identical or slightly different because the aim is to widen the analysis window to automatically identify the characteristic activity within the third video stream.

[0085] According to an example, a plurality of first extractions EXTi and detections DETi are carried out successively by refining a sampling over increasingly fine time periods around a date of interest linked to the specific change of a deposit OBi. In this case, at the Nth detection DETi, the second extraction EXT2 is initiated.

[0086] The first equipment 10 implements a new extraction step EXT2 making it possible to extract a second series of images S2. The second series of images S2 advantageously comprises finer sampling, i.e. with a higher frequency than the first sampling fei in order to extract over the first time window Di a greater number of images in order to carry out a second detection DET2 of an activity characteristic of a human activity ACTi in the temporal vicinity of the first detection DETi. One interest is to verify that the deposit OBi is associated with a deposit of an object by a human.

[0087] According to one example, the method of the invention correlates: - the detection of an OBi deposit detected with - a second DET2 detection of an activity characteristic of human activity ACT i

[0088] Thus, such an operation makes it possible not to generate additional steps of the method and makes it possible to lighten the calculations of the Ki calculator(s) of the first equipment 10. Furthermore, this makes it possible to reinforce the robustness of the method by combining the generation of several data coming from different equipment or components to refine the detection criteria.

[0089] For this purpose, the method of the invention comprises a third transmission TR3 aimed at transmitting the second series of images S2 to a computer responsible for this detection of ACTp activity. Different embodiments of the method of the invention can be implemented. The second series of S2 images can be processed locally by the Ki computer or by other equipment on site within a second K2 computer or by a remote SERV2 server.

[0090] The second series of images S2 makes it possible to initiate a second processing aimed at detecting a characteristic activity ACTi in the vicinity of the date ti corresponding to the first detection of an OBi deposit.

[0091] The detection of a characteristic activity ACTi can be implemented by means of a first learning function FAi implementing a machine learning model. According to one example, a CNN type neural network, designating "convolutional neural network" in English terminology, can be used. According to another example, a RNN type neural network, designating "recurrent neural network" in English terminology, can be implemented. Many other learning models can be implemented, in particular those adapted to image processing. For this purpose, a machine learning model comprising a parameterization such as a series of coefficients can be learned by means of gradient descent to train a regression model. This first learning function FAi makes it possible in particular to parameterize a classifier in order to classify the different characteristic activities detected.One interest is to detect the characteristic ACTi activities of a deposit of an object by a human in the area corresponding to the detection of an OBi deposit.

[0092] To this end, learning makes it possible to label activities that are not characteristic of fly-tipping and to label activities that are characteristic of fly-tipping. According to one example, sequences of car approaches without an individual getting out of the car can be classified as not characteristic of fly-tipping. According to another example, a collection of fly-tipping can also be labeled as not characteristic of fly-tipping. Conversely, sequences of approach of an individual getting out of a vehicle with an object near an area of ​​interest can correspond to a label of a characteristic activity ACTh

[0093] When a characteristic activity ACTi is identified by the first learning function FAi, a set of data is generated to be transmitted to the first computer Ki or to the first equipment 10. The data comprises at least one image identifier IM2 in which a characteristic activity ACTi is identified and possibly a time code or a date t2 in the second series of images S2. According to one example, at least one geometric zone of the image delimiting a contour linked to the characteristic activity or even a region of interest of the image IM2 can be transmitted to the first equipment 10.

[0094] According to one example, service authentication parameters stored in a memory are automatically generated to secure transmissions between the equipment, for example between the SERVi server and equipment 10.

[0095] The method of the invention comprises the definition of a second time window D 2. This step can advantageously be carried out by the calculator Ki of the equipment 10. The second time window D2 advantageously comprises a period comprising the two dates ti and t2 so as to extract a portion of the first video stream F i comprising a sequence corresponding to the detection of the characteristic activity ACTi and a portion of a sequence corresponding to a deposit OBi. The video portion extracted from the first video stream FLi can have a duration ranging from a few seconds to a few minutes.

[0096] The step of extracting the video portion is denoted EXT3 in [Fig. 3]. The extraction may include in particular the reading of a portion of interest of the first video stream FLi which is recorded in a memory of the first equipment 10 and a recording of the extracted portion on another memory intended to collect and save all the extracted portions of interest in which detections have been annotated. According to a first example case, the extracted portion is extracted with an original sampling Fei of the first video stream FLh according to a second example, a third sampling Fe3 is carried out so as to extract a portion with fewer images so as to promote the creation of a file of reduced size. According to one example, the extracted video is compressed according to an image compression algorithm.

[0097] The method of the invention comprises a new step of transmitting TR5 the extracted video portion. The transmission of this extract is preferably carried out to another server SERV2. To be used by an image analysis service.

[0098] The second server SERV2 comprises a step for recording the extracted video portion. This step may be included in the method of the invention or may be carried out by another method. In other words, this step is optional and may not be considered as part of the invention.

[0099] The second server SERV2 comprises the execution of a new step noted Ni which consists of the emission of a notification Ni comprising the transmission of the data characteristic of the detection, such as a qualification of the illegal dumping, the time and date of the illegal dumping, an identifier of the area of ​​interest and the identification of a license plate.

[0100] The detection of the license plate can be carried out by the second server SERV2 in particular by the implementation of a detection algorithm for detecting the license plate and an algorithm aimed at interpreting the number of the license plate.

[0101] Such an algorithm may in particular comprise the implementation of a third learning function FA3 making it possible to detect a car and a zone of interest cor corresponding to the area generally reserved on the car for affixing a registration plate, that is to say on the rear of the car and / or on the front of the car or a trailer where applicable and placed above the bumper of the latter.

[0102] For this purpose, the detection of a license plate can be implemented by means of a third learning function FA3 implementing a machine learning model. According to one example, a CNN type neural network, designating "convolutional neural network" in English terminology, can be used. According to another example, a RNN type neural network, designating "recurrent neural network" in English terminology, can be implemented. Many other learning models can be implemented, in particular those adapted to image processing. For this purpose, a machine learning model comprising a parameterization such as a series of coefficients of a function or weights of a matrix or any other parameterization of a learning function, can be learned by means of gradient descent to train a regression model.This third learning function FA3 allows in particular to configure a classifier in order to classify license plate images. One interest is to isolate the image of interest and to extract the image of the license plate of the car.

[0103] According to an exemplary embodiment, a step aimed at correlating the detection of the vehicle whose license plate is to be obtained with the detection of the characteristic activity ACTi. This step makes it possible to remove ambiguities in the image of the vehicle used when several vehicles are present in the scene. The image comprising the identification of an individual of a vehicle at the time of detection of the characteristic activity can be advantageously used for this purpose.

[0104] A second algorithm for extracting the license plate number may include, in particular, a character recognition algorithm for a portion of the image. Thus, the license plate number may be obtained. According to an exemplary embodiment, an image transformation algorithm may be applied so as to generate an image of the license plate in an undistorted reference frame. The deformation may be linked to the viewing angle of the camera, thus, the transformation parameters may be automatically generated as a function of the position of the car in the scene, i.e. in the acquired image and according to the orientation of the latter in the image.

[0105] According to one embodiment, the algorithm for extracting the license plate number can be performed by the first equipment 10, for example by the first computer Ki or by another computer of the same equipment. According to one example, a second equipment on the site of the area of ​​interest which is paired or connected to the first equipment can perform the algorithm for extracting the plate number, for example from the computer K2. According to another example, the server SERVi performs this step of extracting the plate number of the car parked at proximity to the scene during the illegal dumping.

[0106] The detection of a vehicle and the positioning of the license plate can advantageously be used to blur said license plate when the images are extracted from the first video stream FLi to be transmitted either to another computer of the first equipment 10, or to a second computer K2 of a second equipment arranged on the site of the area of ​​interest Zi such as equipment connected to the first equipment 10. According to another example, the area of ​​the plates is blurred when the images are transmitted to the first remote server SERVi.

[0107] According to one embodiment, the blurring is carried out upon reception of the first series of images Si and / or the second series of images S2. By the equipment which receives said images, for example K2 or SERVi.

[0108] [Fig.4] represents an example of equipment 10 comprising a camera Ci configured to acquire images. The acquired video stream is recorded in a memory Mi and the calculator Ki of the equipment 10 makes it possible to process the video stream to sample the video stream. According to one embodiment, the calculator Ki or another can be configured so as to segment the video stream. According to one embodiment, the calculator Ki or another can be configured to carry out digital pre-processing on the images, such as image compression, image correction, filter application or noise processing.

[0109] According to one embodiment, the equipment 10 comprises a communication interface INTC making it possible to transfer information such as images to a remote server. The interface may be a 3G, 4G, Wifi, Bluetooth interface or any other type of communication interface.

[0110] [Fig.5] represents different representations of the video streams, in particular the video stream acquired FLi at the frequency feO. The frequency of the video stream is generally between 1 image / second and 30 images / second, noted respectively li / s and 30i / s, however other periods can be used depending on the equipment used. According to one embodiment, sampling at acquisition is carried out in order to reduce the weight of the recorded videos, for example to obtain a sampling frequency between 1 image per minute and 1 image per second, noted respectively li / s and li / m.

[0111] [Fig.5] represents the second video stream FL2 corresponding to a series of images extracted from the first video stream FLi sampled with a predefined number of images per second. The sampling frequency Fei for extracting images from the first stream FLi to produce the third stream FL2 is noted Fc,. This number of extracted images can be between 1 image per second and 1 image per hour. According to other examples, other samplings can be chosen. The second video stream can correspond to a continuous stream extracted in real time from the first FLi video stream or it can correspond to time segments of the first FLb video stream

[0112] [Fig.5] also represents the third video stream FL3 corresponding to a series of images extracted from the first video stream FLi sampled with a predefined number of images per second. The sampling frequency for extracting images from the first stream FLi to produce the third stream FL3 is noted Fe2. This number of extracted images can be between 1 image per second and 30 images per second. According to other examples, other samplings can be chosen.

[0113] [Fig.5] makes it possible to represent the time windows Di and D2 for analyzing the flow of images FLi according to the dates of detection of a deposit OBi, date tb and / or of a characteristic activity, date t2.

[0114] These time windows D2 make it possible to generate an analysis window of the first video stream FLi with another sampling adapted to extract either from the images to be analyzed a portion of a video to be transmitted to the second server SERV2.

[0115] The first time window Di is for example centered on the date ti of detection of a deposit OBi. This time window makes it possible to analyze a video portion in which images from the first stream are extracted with a second sampling frequency fe2. When a characteristic activity ACTh is detected, a second time window D2 is defined for example with respect to the dates ti and t2 so as to extract a video portion from the first video stream FLi to be sent to the second server SERV2.

[0116] [Fig.6] represents an illustration of the data flows between each piece of equipment in the system, here represented by the computers. It is understood that each computer or server is associated with electronic components enabling them to be powered, memories for recording data and interfaces for transmitting certain data.

[0117] Here the transmissions TRi and TR3 make it possible to transfer series of images Si, S2 resulting from a sampling of the first video stream FLi. The second series of images S2 is included in a time window Db

[0118] The transmissions TR2 and TR4 make it possible to transfer data associated with the detection of OBi deposits and characteristic activity ACTi such as the images IMb IM2 and at least one date tb t2.

[0119] The transmission TR5 makes it possible to transmit the portion of interest of the first video stream FLi included in a second time interval D2. Within which the detections of deposits OBi and characteristic activity ACTi have been carried out.

[0120] [Fig.6] makes it possible to represent within the K2 calculator or the first SERVi server, the blurring algorithms carried out by the FCTi function of the two series of images Si and S2. The notations Si' and S2' of [Fig.6] correspond to the images blurred or at least images containing a blurred portion of the image. The rest of the description has been described with regard to the notations Si and S2 which can be considered as blurred or unblurred images.

[0121] [Fig.6] also represents the DETi or DET2 detection functions, respectively of an OBi deposit and of a characteristic ACTi activity previously described.

Claims

1. Claims Computer-implemented method for detecting video sequences of interest from an area under surveillance comprising: • Acquisition (ACQi) of a first video stream (FLi) by at least one camera (10) arranged to capture images of an area of interest (Zi); • Recording (ENRi) of the first video stream (FLi) in a memory (Mi), said recordings corresponding to recorded videos of a predefined duration; • First extraction (EXTi) of a first series of images (Si) from the first video stream (FLi) defining a second stream of images (FL2) acquired according to a first sampling frequency (FeO by a first computer (KJ of a first device (10); • Transmission (TRJ) of the second image stream (FL2) to a second computer (K2, SERVi); • Detection of a (DETO deposit (OBi) by implementing a first algorithm for detecting changes in the images applied to the second image stream (FL2), said first algorithm being configured to determine a first position (POSi) of a new element (20) in said image, called first element (20), persisting for a minimum duration in at least one image of the second image stream (FU); • Transmission (TR2) to the first equipment (10) of a first detection data item (BINi) relating to the presence of a first element (20) in the image of the second image stream (FL2), of an identifier of a first image (IMi) associated with a first date (ti), said first image (IMi) comprising the first element (20) detected; • Second extraction (EXT2) by a component of the first equipment (10) of a second series of images (S2) of the first video stream (FLi) acquired according to a second sampling frequency (Fe2) over a first time window (D i) comprising the first date (ti), the second series of images (S2) defining a third video stream (FL3); • Transmission (TR3) to the second computer (K2, SERVi) or to a third calculator (K2, SERVi) of the second series of images (S2); • Detection (DET2) of the presence of a human being (ACTi) within at least one image (IM2) of the second series of images (S2) by implementing a first learning function (FAi) in a predefined area around the first position (POSi) of each processed image of the second series of images (S2); • Transmission (TR4) to the first equipment (10) of a second detection data item (BIN2) relating to the presence of a human being (ACTi) in at least one image (IM2) of the second series of images (S2), of an identifier of a second image (IM2) associated with a second date (t2), said second image (IM2) comprising a presence of a human being (ACTi) detected; • Transmission (TR5) of an extract from the first video stream (FLi), a second time window (D2) corresponding to the duration of the extract including the first date (ti) and the second date (t2) to a second remote server (SERV2), the method being characterized in that the second sampling frequency (Fe2) is strictly greater than the first sampling frequency (Fel), said first time window (Dl) comprising a portion of the image stream preceding the first date (tl).

2. Method according to claim 1 characterized in that the method comprises: • Blurring of at least a portion of each image of the first series of images (Si) by implementing a blurring function (FCTi), said blurring being carried out by the second computer (K2, SERVi) after the transmission (TRi) of the second stream of images (FL2) to said second computer (K2, SERVi); • Blurring of at least one image portion of each image of the second series of images (S2) by implementing a blurring function (FCTi), said blurring being carried out by the second or third computer (K2, SERVi) after the transmission (TR3) of the second image stream (FL2) to said second computer (K2, SERVi).

3. Method according to claim 2 characterized in that the second computer (K2, SERVi) is a computer of a remote server (SERVi), said remote server (SERV i) comprising a first blurring function (FCTi) for automatically blurring a second area of interest (Z2) of each image received from the stream of received images (FL2), said first software function (FCTi) comprising the implementation of a machine learning model (MAM) configured to detect and classify license plates and faces, said configuration comprising a machine learning model (MAM) learned from a training data set, said first software function (FCTi) further comprising an algorithm for blurring said detected second area of interest (Z2).

4. Method according to claim 1 characterized in that the detection (DET i) of a deposit (OBi) comprises: • A detection of a change between at least two consecutive images of the second image stream (FL2), said change characterizing a first element (20) present in the image; • A detection of the presence of the first element (20) within a plurality of images of the second image stream (FL2), said images being considered previously and / or successively to the image within which a change was detected; • An extraction of the position of said first element (20) within the image.

5. Method according to claim 4 characterized in that the detection of the presence of the first element (20) within a plurality of images of the second image stream is carried out by performing a first average of pixel values considered in each image of a first subset of images preceding the image within which a change has been detected and by performing a second average of pixel values considered in each image of a second subset of images following the image within which a change was detected, the difference between the first and second average allowing the presence of a deposit to be deduced (OBi).

6. Method according to claim 1 characterized in that the detection of a deposit (OBi) comprises at least one comparison of characteristic properties of a grouping of pixels between at least two images of the first series of images (Si).

7. Method according to claim 1 characterized in that during a detection of a first element (20), the first calculator (Kb SERVi) comprises: • A generation of a first detection data item (BINi) relating to the presence of a first element (20) in the image, • An identification of the first image (IMJ comprising the first element (20) detected in the image either by an image identifier (ID_IMi), or by a date (q) described by a time code in a predefined time window; • the generation of at least a first position (POSi) of said first element (20) in the image.

8. Method according to claim 1 characterized in that the first date (ti) corresponds to a time code of an image in the first image stream (FLi), that is to say to that of an image sampled at the first frequency (Fei).

9. Method according to claim 1 characterized in that the detection (DET i) of the deposit (OBi) comprises the identification of a time interval between two dates (tiA, tiB) between which the deposit (OBi) is detected and in that the detection of a human presence (DET2) comprises the identification of a time interval between two dates (t2A, t2B) between which the presence of a human being (ACTi) is detected.

10. Method according to claim 9 characterized in that the first time window (Di) of the second extraction (EXT2) is defined by the two dates of the time interval identified during the detection (DETi) of the deposit (OBi).

11. Method according to claim 10 characterized in that the second time window (D2) of the second extraction (EXT2) is defined by the two dates of the time interval identified during the detection (DETi) of the deposit (OBi).

12. Method according to claim 1, characterized in that the detection of a human presence comprises the detection of an activity characteristic of a human being.

13. Method according to claim 12, characterized in that the detection of a characteristic activity comprises the implementation of a first learning function (FAi) receiving as input images from the first video stream (FLi) and implementing a machine learning model learned from a set of training data making it possible to classify images of an individual and to classify movements of said individuals in a given portion of each processed image.

14. Method according to claim 1 or 12, characterized in that the second date (t2) corresponds to a date of an image (IM2) sampled and selected in a second time window (D3) corresponding to the period during which a human presence or a characteristic activity was detected.

15. Method according to claim 1 or 12, characterized in that the second transmission (TR2) also comprises the transmission of at least a first position (POSi) of said first element (20) detected in the image (IMi) and in that the fourth transmission (TR4) also comprises the transmission of at least a second position (POS2) of said human presence or said characteristic activity (ACTi) detected in the identified image (IM2).

16. Method according to claim 1, characterized in that the first transmission (TRi) and the transmission (TR3) are carried out to the same computer (K2, SERVI), the second computer and the third computer being in this case the same computers.

17. Equipment (10) intended to be fixed to a mast, said equipment (10) comprising a camera for acquiring images of an area of interest and at least a first computer (Kb K2) and a memory (MJ) for implementing the steps of the method of any one of claims 1 to 16.

18. System comprising equipment (10) comprising a camera for acquiring images of an area of interest and at least a first computer (Ki) and a memory (Mi), said system further comprising a remote server (SERVi) for implementing the steps of the method of any one of claims 1 to 16.

Images