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• How to Track Website Visitors : Benefits, Tools and FAQs

  31 août 2023, par Erin — Analytics Tips, Marketing

  Businesses spend a ton of time, money and effort into creating websites that are not only helpful and captivating, but also highly effective at converting visitors. They’ll create content, revise designs, add new pages and change forms, all in the hope of getting visitors to stay on the site and convert into leads or customers.

  When you track website visitors, you can see which of your efforts are moving the needle. While many people are familiar with pageviews as a metric, website visitor tracking can be much more in-depth and insightful.

  In this article, we’ll cover how website visitor tracking works, what you can track, and how this information can improve sales and marketing results. We’ll also explain global privacy concerns and how businesses can choose the right tracking software. 

  What is website visitor tracking ? 

  Website visitor tracking uses software and applications to track and analyse how visitors interact with your website. It’s a vital tool to help businesses understand whether their website design and content are having the desired effect.

  

  Website visitor tracking includes very broad, non-specific data, like how many times visitors have come to your site. But it can also get very specific, with personal information about the user and even recordings of their visit to your site. Site visits, which may include visiting several different pages of the same site, are often referred to as “sessions.”

  Although Google Analytics is the most widely used website visitor tracking software, it isn’t the most comprehensive or powerful. Companies that want a more in-depth understanding of their website may need to consider running a more precise tool alongside Google Analytics, like Matomo.

  As we’ll cover later, website tracking has many important business applications, but it also poses privacy and security concerns, causing some states and countries to impose strict regulations. Privacy laws and your company’s values should also impact what web analytics tool you choose.

  How website tracking works

  Website tracking starts with the collection of data as users interact with the website. Tracking technologies like cookies, JavaScript and pixels are embedded into web pages. These technologies then gather data about user behaviour, session details and user actions, such as pageviews, clicks, form submissions and more.

  More advanced tracking systems assign unique identifiers (such as cookies or visitor IDs) to individual users. This enables tracking of user journeys across multiple sessions and pages. These detailed journeys can often tell a different story and provide different insights than aggregated numbers do. 

  All this collected data is transmitted from the user’s browser to a centralised tracking system, which can be a third-party web analytics tool or a self-hosted solution. The collected data is stored in databases and processed to generate meaningful insights. This process involves organising the data, aggregating metrics, and creating reports.

  Analytics tools process the collected data to generate reports and visualisations that provide insights into user behaviour. Metrics such as pageviews, bounce rates, conversion rates and user paths are analysed. Good web analytics tools are able to present these insights in a user-friendly way. Analysts and marketing professionals then use this knowledge to make informed decisions to improve the user experience (UX).

  Advanced tracking systems allow data segmentation and filtering based on various criteria, such as user demographics, traffic sources, devices and more. This enables deeper analysis of specific user groups. For example, you might find that your conversion rate is much lower when your website is viewed on a mobile device. You can then dig deeper into that segment of data to find out why and experiment with changes that might increase mobile conversions.

  3 types of website tracking and their benefits

  There are three main categories of website tracking, and they each provide different information that can be used by sales, marketing, engineering and others. Here, we cover those three types and how businesses use them to understand customers and create better experiences.

  Website analytics 

  Website analytics is all about understanding the traffic your website receives. This type of tracking allows you to learn how the website performs based on pageviews, real-time traffic, bounce rate and conversions. 

  For example, you would use website analytics to determine how effectively your homepage drives people toward a product or pricing page. You can use pageviews and previous page statistics to learn how many people who land on your homepage read your blog posts. From there, you could use web analytics to determine the conversion rate of the call to action at the end of each article.

  

  User behaviour

  While website analytics focuses on the website’s performance, user behaviour tracking is about monitoring and quantifying user behaviour. One of the most obvious aspects of user behaviour is what they click on, but there are many other actions you can track. 

  The time a user spends on a page can help you determine whether the content on the page is engaging. Some tracking tools can also measure how far down the page a user scrolls, which reveals whether some content is even being seen. 

  Session recordings are another popular tool for analysing user behaviour. They not only show concrete actions, like clicks, but can also show how the user moves throughout the page. Where do they stop ? What do they scroll right past ? This is one example of how user behaviour data can be quantitative or qualitative.

  Visitor information

  Tracking can also include gathering or uncovering information about visitors to your site. This might include demographic information, such as language and location, or details like what device a website visitor is using and on which browser they view your website. 

  This type of data helps your web and marketing teams make better decisions about how to design and format the site. If you know, for example, that the website for your business-to-business (B2B) software is overwhelmingly viewed on desktop computers, that will affect how you structure your pages and choose images. 

  Similarly, if visitor information tells you that you have a significant audience in France, your marketing team might develop new content to appeal to those potential customers.

  Use website visitor tracking to improve marketing, sales and UX 

  Website visitor tracking has various applications for different parts of your business, from marketing to sales and much more. When you understand the impact tracking has on different teams, you can better evaluate your company’s needs and build buy-in among stakeholders.

  Marketing

  At many companies, the marketing team owns and determines what kind of content is on your website. From landing pages to blog posts to the navigation bar, you want to create an experience that drives people toward making a purchase. When marketers can track website visitors, they can get a real look at how visitors respond to and engage with their marketing efforts. Pageviews, conversion rates and time spent on pages help them better understand what your customers care about and what messaging resonates.

  But web analytics can even help marketing teams better understand how their external marketing campaigns are performing. Tracking tools like Matomo reveal your most important traffic sources. The term “traffic source” refers to the content or web property from which someone arrives at your site. 

  For instance, you might notice that an older page got a big boost in traffic this month. You can then check the traffic sources, where you find that an influential LinkedIn user posted a link to the page. This presents an opportunity to adjust the influencer or social media aspects of your marketing strategy.

  

  Beyond traffic sources, Matomo can provide a visual user journey (also known as User Flow), showing which pages visitors tend to view in a session and even in what order they progress. This gives you a bird’s-eye view of the customer journey.

  Sales

  Just like your marketing team, your sales team can benefit from tracking and analysing website visitor information. Data about user behaviour and visitor demographics helps sales representatives better understand the people they’re talking to. Segmented visitor tracking data can even provide clues as to how to appeal to different buyer personas.

  Sales leadership can use web analytics to gauge interest over time, tie visitors to revenue and develop more accurate sales forecasts and goals. 

  

  And it’s not just aggregated website tracking data that your sales team can use to better serve customers. They can also use insights about an individual visitor to tailor their approach. Matomo’s Visits Log report and Visitor Profiles allow you to see which pages a prospect has viewed. This tells your sales team which products and features the prospect is most interested in, leading to more relevant interactions and more effective sales efforts.

  User experience and web development

  The way users interact with and experience your website has a big impact on their impression of your brand and, ultimately, whether they become customers. While marketing often controls much of a website’s content, the backend and technical operation of the site usually falls to a web development or engineering team. Website analytics and tracking inform their work, too.

  Along with data about website traffic and conversion rates, web development teams often monitor bounce rates (the percentage of people who leave your website entirely after landing on a page) and page load time (the time it takes for an individual web page to load for a user). Besides the fact that slow loading times inconvenience visitors, they can also negatively affect your search engine optimization (SEO).

  

  Along with session recordings, user experience teams and web developers may use heatmaps to find out what parts of a page draw a visitor’s attention and where they are most likely to convert or take some other action. They can then use these insights to make a web page more intuitive and useful.

  Visitor tracking and privacy regulations 

  There are different data privacy standards in other parts of the world, which are designed to ensure that businesses collect and use consumer data ethically. The most-discussed of these privacy standards is the General Data Protection Regulation (GDPR), which was instituted by the European Union (EU) but affects businesses worldwide. However, it’s important to note that individual countries or states can have different privacy laws.

  Many privacy laws govern how websites can use cookies to track visitors. With a user’s consent, cookies can help websites identify and remember visitors. However, many web visitors will reject cookie consent banners. When this happens, analysts and marketers can’t collect information from these visitors and have to work with incomplete tracking data. Incomplete data leads to poor decision-making. What’s more, cookie consent banners can create a poor user experience and often annoy web visitors.

  With Matomo’s industry-leading measures to protect user privacy, France’s data protection agency (CNIL) has confirmed that Matomo is exempt from tracking consent in France. Matomo users have peace of mind knowing they can uphold the GDPR and collect data without needing to collect and track cookie consent. Only in Germany and the UK are cookie consent banners still required.

  Choosing user tracking software

  The benefits and value of tracking website visitors are enormous, but not all tracking software is equal. Different tools have different core functionalities. For instance, some focus on user behaviour over traditional web analytics. Others offer detailed website performance data but offer little in the way of visitor information. It’s a good idea to start by identifying your company’s most important tracking goals.

  Along with core features, look for useful tools to experiment with and optimise your website with. For example, Matomo enables A/B testing while many other tools do not.

  

  Along with users of your website, you also need to think about the employees who will be using the tracking software. The interface can have a big impact on the value you get from a tool. Matomo’s session recording functionality, for example, not only provides you with video but with a colour-coded timeline identifying important user actions.

  

  Privacy standards and compliance should also be a part of the conversation. Different tools use different tracking methods, impacting accuracy and security and can even cause legal trouble. You should consider which data privacy laws you are subject to, as well as the privacy expectations of your users.

  

  Some industries have especially high data security standards. Government and healthcare organisations, for example, may require visitor tracking software that is hosted on their premises. While there are many purely cloud-based software-as-a-service (SaaS) tracking tools, Matomo is available both On-Premise (also known as self-hosted) and in the Cloud.

  Frequently asked questions

  Here are answers to some of people’s most common questions about tracking website visitors.

  Can you track who visited your website ?

  In most cases, tracking your website’s traffic is possible. Still, the extent of the tracking depends on the visitor-tracking technology you use and the privacy settings and precautions the visitor uses. For example, some technologies can pinpoint users by IP address. In other cases, you may only have access to anonymized data.

  Is it legal to track someone’s IP address ?

  It is legal for websites and businesses to track someone’s IP address in the sense that they can identify that someone from the same IP address is visiting a page repeatedly. Under the General Data Protection Regulation (GDPR), IP addresses are considered personally identifiable information (PII). The GDPR mandates that websites only log and store a user’s IP address with the user’s consent.

  How do you find where visitors are clicking the most ?

  Heatmap tools are among the most common tools for learning where visitors click the most on your website. Heatmaps use colour-coding to show what parts of a web page users either click on or hover over the most.

  Unique tracking URLs are another way to determine what part of your website gets the most clicks. For example, if you have three links on a page that all go to the same destination, you can use tracking links to determine how many clicks each link generates.

  Matomo also offers a Tag Manager within the platform that lets you manage and unify all your tracking and marketing tags to find out where visitors are clicking.

  What is the best tool for website visitor tracking ?

  Like most tools, the best website visitor tracking tool depends on your needs. Each tool offers different functionalities, user interfaces and different levels of accuracy and privacy. Matomo is a good choice for companies that value privacy, compliance and accuracy.

  Tracking for powerful insights and better performance

  Tracking website visitors is now a well-ingrained part of business operations. From sales reps seeking to understand their leads to marketers honing their ad spend, tracking helps teams do their jobs better.

  Take the time to consider what you want to learn from website tracking and let those priorities guide your choice of visitor tracking software. Whatever your industry or needs, user privacy and compliance must be a priority.

  Find out how much detail and insight Matomo can give you with our free 21-day trial — no credit card required.

• pyqt5 gui dependent on ffmpeg compiled with pyinstaller doesn't run on other machines ?

  19 octobre 2022, par Soren

  I am trying to create a simple Pyqt5 GUI for Windows 10 that uses OpenAI's model Whisper to transcribe a sound file and outputting the results in an Excel-file. It works on my own computer where I have installed the necessary dependencies for Whisper as stated on their github i.e. FFMEG. I provide a minimal example of my code below :

  


  # Import library
import whisper
import os
from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
import xlsxwriter


class Ui_Dialog(QtWidgets.QDialog):
    
    
    # Define functions to use in GUI
   
    # Define function for selecting input files
    def browsefiles(self, Dialog):
      
       
       # Make Dialog box and save files into tuple of paths
       files = QtWidgets.QFileDialog().getOpenFileNames(self, "Select soundfiles", os.getcwd(), "lyd(*mp2 *.mp3 *.mp4 *.m4a *wma *wav)")
       
       self.liste = []
       for url in range(len(files[0])):
           self.liste.append(files[0][url])   

    
    def model_load(self, Dialog):
               
        # Load picked model
        self.model = whisper.load_model(r'C:\Users\Søren\Downloads\Whisper_gui\models' + "\\" + self.combo_modelSize.currentText() + ".pt") ##the path is set to where the models are on the other machine
        
    
    def run(self, Dialog):
                
        # Make list for sound files
        liste_df = []
        
        
        # Running loop for interpreting and encoding sound files
        for url in range(len(self.liste)):
                          
            # Make dataframe
            df = pd.DataFrame(columns=["filename", "start", "end", "text"])
            
            # Run model
            result = self.model.transcribe(self.liste[url])
                            
            # Extract results
            for i in range(len(result["segments"])):
                start = result["segments"][i]["start"]
                end = result["segments"][i]["end"]
                text = result["segments"][i]["text"]
                
                df = df.append({"filename": self.liste[url].split("/")[-1],
                            "start": start, 
                            "end": end, 
                            "text": text}, ignore_index=True)
            
            # Add detected language to dataframe
            df["sprog"] = result["language"]
            
            
            liste_df.append(df)
        
        
        
        # Make excel output
        
        # Concatenate list of dfs
        dataframe = pd.concat(liste_df)
        
        
        # Create a Pandas Excel writer using XlsxWriter as the engine.
        writer = pd.ExcelWriter(self.liste[0].split(".")[0] + '_OUTPUT.xlsx', engine='xlsxwriter')
        writer_wrap_format = writer.book.add_format({"text_wrap": True, 'num_format': '@'})


        # Write the dataframe data to XlsxWriter. Turn off the default header and
        # index and skip one row to allow us to insert a user defined header.
        dataframe.to_excel(writer, sheet_name="Output", startrow=1, header=False, index=False)

        # Get the xlsxwriter workbook and worksheet objects.
        #workbook = writer.book
        worksheet = writer.sheets["Output"]

        # Get the dimensions of the dataframe.
        (max_row, max_col) = dataframe.shape

        # Create a list of column headers, to use in add_table().
        column_settings = [{'header': column} for column in dataframe.columns]

        # Add the Excel table structure. Pandas will add the data.
        worksheet.add_table(0, 0, max_row, max_col - 1, {'columns': column_settings})

        # Make the columns wider for clarity.
        worksheet.set_column(0, max_col - 1, 12)
        
        in_col_no = xlsxwriter.utility.xl_col_to_name(dataframe.columns.get_loc("text"))
        
        worksheet.set_column(in_col_no + ":" + in_col_no, 30, writer_wrap_format)

        # Close the Pandas Excel writer and output the Excel file.
        writer.save()
        writer.close()
    
    
    ## Design setup
    
    def setupUi(self, Dialog):
        Dialog.setObjectName("Dialog")
        Dialog.resize(730, 400)
        
        self.select_files = QtWidgets.QPushButton(Dialog)
        self.select_files.setGeometry(QtCore.QRect(40, 62, 81, 31))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.select_files.setFont(font)
        self.select_files.setObjectName("select_files")
        
    
               
        
        self.combo_modelSize = QtWidgets.QComboBox(Dialog)
        self.combo_modelSize.setGeometry(QtCore.QRect(40, 131, 100, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.combo_modelSize.setFont(font)
        self.combo_modelSize.setObjectName("combo_modelSize")
               
        
        self.runButton = QtWidgets.QPushButton(Dialog)
        self.runButton.setGeometry(QtCore.QRect(40, 289, 71, 21))
        font = QtGui.QFont()
        font.setPointSize(6)
        self.runButton.setFont(font)
        self.runButton.setObjectName("runButton")
        
        
       

        self.retranslateUi(Dialog)
        QtCore.QMetaObject.connectSlotsByName(Dialog)
        
        
        
        modelSize_options = ['Chose model', 'tiny', 'base', 'small', 'medium', 'large']
        self.combo_modelSize.addItems(modelSize_options)
        
        # Do an action!
        self.select_files.clicked.connect(self.browsefiles)
        self.combo_modelSize.currentIndexChanged.connect(self.model_load)
        self.runButton.clicked.connect(self.run)
        
        
        
    

    def retranslateUi(self, Dialog):
        _translate = QtCore.QCoreApplication.translate
        Dialog.setWindowTitle(_translate("Dialog", "Dialog"))
        self.runButton.setText(_translate("Dialog", "Go!"))
        self.select_files.setText(_translate("Dialog", "Select"))


if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    Dialog = QtWidgets.QDialog()
    ui = Ui_Dialog()
    ui.setupUi(Dialog)
    Dialog.show()
    sys.exit(app.exec_())


  


  I compile this app with pyinstaller using the following code. I had some issues to begin with so I found other with similar problems and ended up with this :

  


  pyinstaller --onedir --hidden-import=pytorch --collect-data torch --copy-metadata torch --copy-metadata tqdm --copy-metadata tokenizers --copy-metadata importlib_metadata --hidden-import="sklearn.utils._cython_blas" --hidden-import="sklearn.neighbors.typedefs" --hidden-import="sklearn.neighbors.quad_tree" --hidden-import="sklearn.tree" --hidden-import="sklearn.tree._utils" --copy-metadata regex --copy-metadata requests --copy-metadata packaging --copy-metadata filelock --copy-metadata numpy --add-data "./ffmpeg/*;./ffmpeg/" --hidden-import=whisper --copy-metadata whisper --collect-data whisper minimal_example_whisper.py

  


  When I take the outputtet dist directory and try to run the app on another Windows machine without FFMPEG installed (or Whisper or any other things), I get the following error from the terminal as I push the "run" button in the app (otherwise the app does run).

  


  C:\Users\Søren>"G:\minimal_example_whisper\minimal_example_whisper.exe"
whisper\transcribe.py:70: UserWarning: FP16 is not supported on CPU; using FP32 instead
Traceback (most recent call last):
  File "minimal_example_whisper.py", line 45, in run
  File "whisper\transcribe.py", line 76, in transcribe
  File "whisper\audio.py", line 111, in log_mel_spectrogram
  File "whisper\audio.py", line 42, in load_audio
  File "ffmpeg\_run.py", line 313, in run
  File "ffmpeg\_run.py", line 284, in run_async
  File "subprocess.py", line 951, in __init__
  File "subprocess.py", line 1420, in _execute_child
FileNotFoundError: [WinError 2] Den angivne fil blev ikke fundet


  


  I suspect this has something to do with FFMPEG not being installed on the other machines system ? Does anyone have an automatic solution for this when compiling the app or can it simply only run on machines that has FFMPEG installed ?

  


  Thanks in advance !

  


• ffmpeg piped output producing incorrect metadata frame count

  8 décembre 2024, par Xorgon

  The short version : Using piped output from ffmpeg produces a file with incorrect metadata.

  


  ffmpeg -y -i .\test_mp4.mp4 -f avi -c:v libx264 - > output.avi to make an AVI file using the pipe output.

  


  ffprobe -v error -count_frames -show_entries stream=duration,nb_read_frames,r_frame_rate .\output.avi

  


  The output will show that the metadata does not match the actual frames contained in the video.

  


  Details below.

  


  ---

  


  Using Python, I am attempting to use ffmpeg to compress videos and put them in a PowerPoint. This works great, however, the video files themselves have incorrect frame counts which can cause issues when I read from those videos in other code.

  


  Edit for clarification : by "frame count" I mean the metadata frame count. The actual number of frames contained in the video is correct, but querying the metadata gives an incorrect frame count.

  


  Having eliminated the PowerPoint aspect of the code, I've narrowed this down to the following minimal reproducing example of saving an output from an ffmpeg pipe :

  


  from subprocess import Popen, PIPE

video_path = 'test_mp4.mp4'

ffmpeg_pipe = Popen(['ffmpeg',
                     '-y',  # Overwrite files
                     '-i', f'{video_path}',  # Input from file
                     '-f', 'avi',  # Output format
                     '-c:v', 'libx264',  # Codec
                     '-'],  # Output to pipe
                    stdout=PIPE)

new_path = "piped_video.avi"
vid_file = open(new_path, "wb")
vid_file.write(ffmpeg_pipe.stdout.read())
vid_file.close()


  


  I've tested several different videos. One small example video that I've tested can be found here.

  


  I've tried a few different codecs with avi format and tried libvpx with webm format. For the avi outputs, the frame count usually reads as 1073741824 (2^30). Weirdly, for the webm format, the frame count read as -276701161105643264.

  


  Edit : This issue can also be reproduced with just ffmpeg in command prompt using the following command :
ffmpeg -y -i .\test_mp4.mp4 -f avi -c:v libx264 - > output.avi

  


  This is a snippet I used to read the frame count, but one could also see the error by opening the video details in Windows Explorer and seeing the total time as something like 9942 hours, 3 minutes, and 14 seconds.

  


  import cv2

video_path = 'test_mp4.mp4'
new_path = "piped_video.webm"

cap = cv2.VideoCapture(video_path)
print(f"Original video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()

cap = cv2.VideoCapture(new_path)
print(f"Piped video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()


  


  The error can also be observed using ffprobe with the following command : ffprobe -v error -count_frames -show_entries stream=duration,nb_read_frames,r_frame_rate .\output.avi. Note that the frame rate and number of frames counted by ffprobe do not match with the duration from the metadata.

  


  For completeness, here is the ffmpeg output :

  


  ffmpeg version 2023-06-11-git-09621fd7d9-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 13.100 / 58. 13.100
  libavcodec     60. 17.100 / 60. 17.100
  libavformat    60.  6.100 / 60.  6.100
  libavdevice    60.  2.100 / 60.  2.100
  libavfilter     9.  8.101 /  9.  8.101
  libswscale      7.  3.100 /  7.  3.100
  libswresample   4. 11.100 /  4. 11.100
  libpostproc    57.  2.100 / 57.  2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'test_mp4.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: isommp42
    creation_time   : 2022-08-10T12:54:09.000000Z
  Duration: 00:00:06.67, start: 0.000000, bitrate: 567 kb/s
  Stream #0:0[0x1](eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], 563 kb/s, 30 fps, 30 tbr, 30k tbn (default)
    Metadata:
      creation_time   : 2022-08-10T12:54:09.000000Z
      handler_name    : Mainconcept MP4 Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : AVC Coding
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0000018c68c8b9c0] using SAR=1/1
[libx264 @ 0000018c68c8b9c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0000018c68c8b9c0] profile High, level 2.1, 4:2:0, 8-bit
Output #0, avi, to 'pipe:':
  Metadata:
    major_brand     : mp42
    minor_version   : 0
    compatible_brands: isommp42
    ISFT            : Lavf60.6.100
  Stream #0:0(eng): Video: h264 (H264 / 0x34363248), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], q=2-31, 30 fps, 30 tbn (default)
    Metadata:
      creation_time   : 2022-08-10T12:54:09.000000Z
      handler_name    : Mainconcept MP4 Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.17.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[out#0/avi @ 0000018c687f47c0] video:82kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 3.631060%
frame=  200 fps=0.0 q=-1.0 Lsize=      85kB time=00:00:06.56 bitrate= 106.5kbits/s speed=76.2x    
[libx264 @ 0000018c68c8b9c0] frame I:1     Avg QP:16.12  size:  3659
[libx264 @ 0000018c68c8b9c0] frame P:80    Avg QP:21.31  size:   647
[libx264 @ 0000018c68c8b9c0] frame B:119   Avg QP:26.74  size:   243
[libx264 @ 0000018c68c8b9c0] consecutive B-frames:  3.0% 53.0%  0.0% 44.0%
[libx264 @ 0000018c68c8b9c0] mb I  I16..4: 17.6% 70.6% 11.8%
[libx264 @ 0000018c68c8b9c0] mb P  I16..4:  0.8%  1.7%  0.6%  P16..4: 17.6%  4.6%  3.3%  0.0%  0.0%    skip:71.4%
[libx264 @ 0000018c68c8b9c0] mb B  I16..4:  0.1%  0.3%  0.2%  B16..8: 11.7%  1.4%  0.4%  direct: 0.6%  skip:85.4%  L0:32.0% L1:59.7% BI: 8.3%
[libx264 @ 0000018c68c8b9c0] 8x8 transform intra:59.6% inter:62.4%
[libx264 @ 0000018c68c8b9c0] coded y,uvDC,uvAC intra: 48.5% 0.0% 0.0% inter: 3.5% 0.0% 0.0%
[libx264 @ 0000018c68c8b9c0] i16 v,h,dc,p: 19% 39% 25% 17%
[libx264 @ 0000018c68c8b9c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 25% 30%  3%  3%  4%  4%  4%  5%
[libx264 @ 0000018c68c8b9c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 20% 16%  6%  8%  8%  8%  5%  6%
[libx264 @ 0000018c68c8b9c0] i8c dc,h,v,p: 100%  0%  0%  0%
[libx264 @ 0000018c68c8b9c0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0000018c68c8b9c0] ref P L0: 76.2%  7.9% 11.2%  4.7%
[libx264 @ 0000018c68c8b9c0] ref B L0: 85.6% 12.9%  1.5%
[libx264 @ 0000018c68c8b9c0] ref B L1: 97.7%  2.3%
[libx264 @ 0000018c68c8b9c0] kb/s:101.19


  


  So the question is : why does this happen, and how can one avoid it ?