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• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.

• A Comprehensive Guide to Robust Digital Marketing Analytics

  30 octobre 2023, par Erin

  First impressions are everything. This is not only true for dating and job interviews but also for your digital marketing strategy. Like a poorly planned resume getting tossed in the “no thank you” pile, 38% of visitors to your website will stop engaging with your content if they find the layout unpleasant. Thankfully, digital marketers can access data that can be harnessed to optimise websites and turn those “no thank you’s” into “absolutely’s.”

  So, how can we transform raw data into valuable insights that pay off ? The key is web analytics tools that can help you make sense of it all while collecting data ethically. In this article, we’ll equip you with ways to take your digital marketing strategy to the next level with the power of web analytics.

  What are the different types of digital marketing analytics ?

  Digital marketing analytics are like a cipher into the complex behaviour of your buyers. Digital marketing analytics help collect, analyse and interpret data from any touchpoint you interact with your buyers online. Whether you’re trying to gauge the effectiveness of a new email marketing campaign or improve your mobile app layout, there’s a way for you to make use of the insights you gain. 

  As we go through the eight commonly known types of digital marketing analytics, please note we’ll primarily focus on what falls under the umbrella of web analytics. 

  1. Web analytics help you better understand how users interact with your website. Good web analytics tools will help you understand user behaviour while securely handling user data. 
  2. Learn more about the effectiveness of your organisation’s social media platforms with social media analytics. Social media analytics include user engagement, post reach and audience demographics. 
  3. Email marketing analytics help you see how email campaigns are being engaged with.
  4. Search engine optimisation (SEO) analytics help you understand your website’s visibility in search engine results pages (SERPs). 
  5. Pay-per-click (PPC) analytics measure the performance of paid advertising campaigns.
  6. Content marketing analytics focus on how your content is performing with your audience. 
  7. Customer analytics helps organisations identify and examine buyer behaviour to retain the biggest spenders. 
  8. Mobile app analytics track user interactions within mobile applications. 

  Choosing which digital marketing analytics tools are the best fit for your organisation is not an easy task. When making these decisions, it’s critical to remember the ethical implications of data collection. Although data insights can be invaluable to your organisation, they won’t be of much use if you lose the trust of your users. 

  Tips and best practices for developing robust digital marketing analytics 

  So, what separates top-notch, robust digital marketing analytics from the rest ? We’ve already touched on it, but a big part involves respecting user privacy and ethically handling data. Data security should be on your list of priorities, alongside conversion rate optimisation when developing a digital marketing strategy. In this section, we will examine best practices for using digital marketing analytics while retaining user trust.

  

  Clear objectives

  Before comparing digital marketing analytics tools, you should define clear and measurable goals. Try asking yourself what you need your digital marketing analytics strategy to accomplish. Do you want to improve conversion rates while remaining data compliant ? Maybe you’ve noticed users are not engaging with your platform and want to fix that. Save yourself time and energy by focusing on the most relevant pain points and areas of improvement.

  Choose the right tools for the job

  Don’t just base your decision on what other people tell you. Take the tool for a test drive — free trials allow you to test features and user interfaces and learn more about the platform before committing. When choosing digital marketing analytics tools, look for ones that ensure compliance with privacy laws like GDPR.

  Don’t overlook data compliance

  GDPR ensures organisations prioritise data protection and privacy. You could be fined up to €20 million, or 4% of the previous year’s revenue for violations. Without data compliance practices, you can say goodbye to the time and money spent on digital marketing strategies. 

  Don’t sacrifice data quality and accuracy

  Inaccurate and low-quality data can taint your analysis, making it hard to glean valuable insights from your digital marketing analytics efforts. Regularly audit and clean your data to remove inaccuracies and inconsistencies. Address data discrepancies promptly to maintain the integrity of your analytics. Data validation measures also help to filter out inaccurate data.

  Communicate your findings

  Having insights is one thing ; effectively communicating complex data findings is just as important. Customise dashboards to display key metrics aligned with your objectives. Make sure to automate reports, allowing stakeholders to stay updated without manual intervention. 

  Understand the user journey

  To optimise your conversion rates, you need to understand the user journey. Start by analysing visitors interactions with your website — this will help you identify conversion bottlenecks in your sales or lead generation processes. Implement A/B testing for landing page optimisation, refining elements like call-to-action buttons or copy, and leverage Form Analytics to make informed, data-driven improvements to your forms.

  Continuous improvement

  Learn from the data insights you gain, and iterate your marketing strategies based on the findings. Stay updated with evolving web analytics trends and technologies to leverage new growth opportunities.

  Why you need web analytics to support your digital marketing analytics toolbox

  You wouldn’t set out on a roadtrip without a map, right ? Digital marketing analytics without insights into how users interact with your website are just as useless. Used ethically, web analytics tools can be an invaluable addition to your digital marketing analytics toolbox. 

  The data collected via web analytics reveals user interactions with your website. These could include anything from how long visitors stay on your page to their actions while browsing your website. Web analytics tools help you gather and understand this data so you can better understand buyer preferences. It’s like a domino effect : the more you understand your buyers and user behaviour, the better you can assess the effectiveness of your digital content and campaigns. 

  Web analytics reveal user behaviour, highlighting navigation patterns and drop-off points. Understanding these patterns helps you refine website layout and content, improving engagement and conversions for a seamless user experience.

  

  Concrete CMS harnessed the power of web analytics, specifically Form Analytics, to uncover a crucial insight within their user onboarding process. Their data revealed a significant issue : the “address” input field was causing visitors to drop off and not complete the form, severely impacting the overall onboarding experience and conversion rate.

  Armed with these insights, Concrete CMS made targeted optimisations to the form, resulting in a substantial transformation. By addressing the specific issue identified through Form Analytics, they achieved an impressive outcome – a threefold increase in lead generation.

  This case is a great example of how web analytics can uncover customer needs and preferences and positively impact conversion rates. 

  Ethical implications of digital marketing analytics

  As we’ve touched on, digital marketing analytics are a powerful tool to help better understand online user behaviour. With great power comes great responsibility, however, and it’s a legal and ethical obligation for organisations to protect individual privacy rights. Let’s get into the benefits of practising ethical digital marketing analytics and the potential risks of not respecting user privacy : 

  • If someone uses your digital platform and then opens their email one day to find it filled with random targeted ad campaigns, they won’t be happy. Avoid losing user trust — and facing a potential lawsuit — by informing users what their data will be used for. Give them the option to consent to opt-in or opt-out of letting you use their personal information. If users are also assured you’ll safeguard personal information against unauthorised access, they’ll be more likely to trust you to handle their data securely.
  • Protecting data against breaches means investing in technology that will let you end-to-end encrypt and securely store data. Other important data-security best practices include access control, backing up data regularly and network and physical security of assets.
  • A fine line separates digital marketing analytics and misusing user data — many companies have gotten into big trouble for crossing it. (By big trouble, we mean millions of dollars in fines.) When it comes to digital marketing analytics, you should never cut corners when it comes to user privacy and data security. This balance involves understanding what data can be collected and what should be collected and respecting user boundaries and preferences.

  

  Learn more 

  We discussed a lot of facets of digital marketing analytics, namely how to develop a robust digital marketing strategy while prioritising data compliance. With Matomo, you can protect user data and respect user privacy while gaining invaluable insights into user behaviour. Save your organisation time and money by investing in a web analytics solution that gives you the best of both worlds. 

  If you’re ready to begin using ethical and robust digital marketing analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

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• FFMPEG : RTSP re-stream dies randomly

  14 mai 2018, par stevendesu

  I have a security camera streaming RTSP, and I wish to re-stream this to an RTMP ingest server. For now I’m using my laptop as an ffmpeg proxy, but eventually I’ll use a raspberry pi or something similar (cheap/small)

  Here’s the command I’m using (pretty simple) :

  ffmpeg -i rtsp://@10.0.0.16:554/1/h264major -c:v libx264 -c:a none -f flv rtmp://output/camera_stream

  This works but after a minute or two the stream dies. Here’s the output :

  ffmpeg version N-90057-g7c82e0f Copyright (c) 2000-2018 the FFmpeg developers
  
    built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.6) 20160609
  
    configuration: --prefix=/home/sbarnett/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/sbarnett/ffmpeg_build/include --extra-ldflags=-L/home/sbarnett/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/sbarnett/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libspeex --enable-nonfree
  
    libavutil      56.  7.101 / 56.  7.101
  
    libavcodec     58. 11.101 / 58. 11.101
  
    libavformat    58.  9.100 / 58.  9.100
  
    libavdevice    58.  1.100 / 58.  1.100
  
    libavfilter     7. 12.100 /  7. 12.100
  
    libswscale      5.  0.101 /  5.  0.101
  
    libswresample   3.  0.101 /  3.  0.101
  
    libpostproc    55.  0.100 / 55.  0.100
  
  Input #0, rtsp, from 'rtsp://@10.0.0.16:554/1/h264major':
  
    Metadata:
  
      title           : h264major
  
      comment         : h264major
  
    Duration: N/A, start: 0.360000, bitrate: N/A
  
      Stream #0:0: Video: h264 (Main), yuvj420p(pc, bt709, progressive), 720x480, 25 fps, 25 tbr, 90k tbn, 50 tbc
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  
  Press [q] to stop, [?] for help
  
  [libx264 @ 0x38843c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
  
  [libx264 @ 0x38843c0] profile High, level 3.0
  
  [libx264 @ 0x38843c0] 264 - core 155 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
  Output #0, flv, to 'rtmp://output/camera_stream':
  
    Metadata:
  
      title           : h264major
  
      comment         : h264major
  
      encoder         : Lavf58.9.100
  
      Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuvj420p(pc), 720x480, q=-1--1, 25 fps, 1k tbn, 25 tbc
  
      Metadata:
  
        encoder         : Lavc58.11.101 libx264
  
      Side data:
  
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
  
  Past duration 0.999992 too large
  
      Last message repeated 29 times
  
  [rtsp @ 0x3847600] max delay reached. need to consume packet
  
  [rtsp @ 0x3847600] RTP: missed 48 packets
  
  Past duration 0.999992 too large
  
      Last message repeated 4 times
  
  frame=   44 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A dup=0 drop=5 speed=   0x    
  
  frame=   57 fps= 54 q=28.0 size=      43kB time=00:00:00.16 bitrate=2186.4kbits/s dup=0 drop=5 speed=0.153x    
  
  ... (lots of similar messages) ...   
  
  frame= 1163 fps= 26 q=28.0 size=    1341kB time=00:00:44.84 bitrate= 245.0kbits/s dup=0 drop=5 speed=0.99x    
  
  frame= 1177 fps= 26 q=28.0 size=    1353kB time=00:00:45.40 bitrate= 244.2kbits/s dup=0 drop=5 speed=0.99x    
  
  [rtsp @ 0x3847600] max delay reached. need to consume packet
  
  [rtsp @ 0x3847600] RTP: missed 2 packets
  
  frame= 1190 fps= 26 q=28.0 size=    1370kB time=00:00:45.92 bitrate= 244.4kbits/s dup=0 drop=5 speed=0.99x    
  
  [h264 @ 0x38c08c0] Increasing reorder buffer to 1
  
  frame= 1201 fps= 26 q=28.0 size=    1381kB time=00:00:46.36 bitrate= 244.0kbits/s dup=0 drop=5 speed=0.989x    
  
  frame= 1214 fps= 26 q=28.0 size=    1393kB time=00:00:46.88 bitrate= 243.4kbits/s dup=0 drop=5 speed=0.989x    
  
  ... (lots of similar messages) ...    
  
  frame= 1761 fps= 25 q=28.0 size=    2030kB time=00:01:08.80 bitrate= 241.7kbits/s dup=0 drop=5 speed=0.993x    
  
  frame= 1774 fps= 25 q=28.0 size=    2041kB time=00:01:09.32 bitrate= 241.2kbits/s dup=0 drop=5 speed=0.993x    
  
  [flv @ 0x3884900] Failed to update header with correct duration.
  
  [flv @ 0x3884900] Failed to update header with correct filesize.
  
  frame= 1782 fps= 25 q=-1.0 Lsize=    2127kB time=00:01:11.64 bitrate= 243.2kbits/s dup=0 drop=5 speed=1.02x    
  
  video:2092kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.679417%
  
  [libx264 @ 0x38843c0] frame I:8     Avg QP:16.89  size: 42446
  
  [libx264 @ 0x38843c0] frame P:1672  Avg QP:19.54  size:  1065
  
  [libx264 @ 0x38843c0] frame B:102   Avg QP:23.00  size:   205
  
  [libx264 @ 0x38843c0] consecutive B-frames: 92.4%  0.0%  0.0%  7.6%
  
  [libx264 @ 0x38843c0] mb I  I16..4: 12.9% 36.2% 50.9%
  
  [libx264 @ 0x38843c0] mb P  I16..4:  0.2%  0.2%  0.0%  P16..4: 16.7%  0.7%  1.0%  0.0%  0.0%    skip:81.1%
  
  [libx264 @ 0x38843c0] mb B  I16..4:  0.1%  0.1%  0.0%  B16..8: 11.7%  0.1%  0.0%  direct: 1.5%  skip:86.5%  L0:62.2% L1:35.3% BI: 2.5%
  
  [libx264 @ 0x38843c0] 8x8 transform intra:40.8% inter:47.4%
  
  [libx264 @ 0x38843c0] coded y,uvDC,uvAC intra: 46.5% 53.0% 17.2% inter: 3.9% 8.7% 0.0%
  
  [libx264 @ 0x38843c0] i16 v,h,dc,p: 21% 56%  8% 15%
  
  [libx264 @ 0x38843c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 33% 31%  1%  2%  3%  2%  2%  3%
  
  [libx264 @ 0x38843c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 25% 39%  9%  3%  3%  4%  5%  3%  8%
  
  [libx264 @ 0x38843c0] i8c dc,h,v,p: 43% 33% 21%  3%
  
  [libx264 @ 0x38843c0] Weighted P-Frames: Y:0.0% UV:0.0%
  
  [libx264 @ 0x38843c0] ref P L0: 88.0%  1.4%  6.6%  4.0%
  
  [libx264 @ 0x38843c0] ref B L0: 99.4%  0.5%  0.1%
  
  [libx264 @ 0x38843c0] ref B L1: 99.4%  0.6%
  
  [libx264 @ 0x38843c0] kb/s:238.73

  The camera is pretty cheap (from China) so it’s likely I’m getting bad data from it or it’s cutting out for a few seconds at a time. Ideally I would need ffmpeg to handle this well (ignore bad data, wait as long as necessary for good data to resume encoding)

  Using ffplay to check out the RTSP stream, I get output like the following :

  $> ffplay -i rtsp://@10.0.0.16:554/1/h264major
  
  ffplay version N-90057-g7c82e0f Copyright (c) 2003-2018 the FFmpeg developers
  
    built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.6) 20160609
  
    configuration: --prefix=/home/sbarnett/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/sbarnett/ffmpeg_build/include --extra-ldflags=-L/home/sbarnett/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/sbarnett/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libspeex --enable-nonfree
  
    libavutil      56.  7.101 / 56.  7.101
  
    libavcodec     58. 11.101 / 58. 11.101
  
    libavformat    58.  9.100 / 58.  9.100
  
    libavdevice    58.  1.100 / 58.  1.100
  
    libavfilter     7. 12.100 /  7. 12.100
  
    libswscale      5.  0.101 /  5.  0.101
  
    libswresample   3.  0.101 /  3.  0.101
  
    libpostproc    55.  0.100 / 55.  0.100
  
  Input #0, rtsp, from 'rtsp://@10.0.0.16:554/1/h264major':0B f=0/0
  
    Metadata:
  
      title           : h264major
  
      comment         : h264major
  
    Duration: N/A, start: 0.320000, bitrate: N/A
  
      Stream #0:0: Video: h264 (Main), yuvj420p(pc, bt709, progressive), 720x480, 25 fps, 25 tbr, 90k tbn, 50 tbc
  
  [swscaler @ 0x7f6bbc093180] deprecated pixel format used, make sure you did set range correctly
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 2 packets
  
  [h264 @ 0x7f6bc0041080] error while decoding MB 44 28, bytestream -37
  
  [h264 @ 0x7f6bc0041080] concealing 95 DC, 95 AC, 95 MV errors in I frame
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 1 packets
  
  [h264 @ 0x7f6bc0041080] error while decoding MB 43 29, bytestream -49
  
  [h264 @ 0x7f6bc0041080] concealing 51 DC, 51 AC, 51 MV errors in I frame
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 2 packets
  
  [h264 @ 0x7f6bc0041080] Increasing reorder buffer to 1
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 3 packets
  
  [h264 @ 0x7f6bc02c3600] error while decoding MB 27 29, bytestream -24
  
  [h264 @ 0x7f6bc02c3600] concealing 67 DC, 67 AC, 67 MV errors in I frame
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 2 packets
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 42 packets
  
  [rtsp @ 0x7f6bc0000940] max delay reached. need to consume packet
  
  [rtsp @ 0x7f6bc0000940] RTP: missed 2 packets

  Then eventually the video just freezes. The first time it froze after around 5 minutes, but I wasn’t able to say definitively if it froze the instant 44 packets were dropped or if it froze randomly later. So the second time I stared intently.... for 21 minutes. Then I got bored of it not freezing, turned to pet my cat, and when I looked back 15 seconds later it was frozen. I think it only breaks when no one is watching it.

  What I can say definitively is :

  • While running normally, M-V hovers around 0 (anywhere between -0.01 and +0.01)
  • Once frozen, M-V begins to count down into negative numbers without stopping - although at a rate slower than -1 per second
  • While running normally, aq is 0KB and vq is a positive number (I think it was 30KB or so ?)
  • Once frozen, vq is also 0KB

  It’s a really cheap camera with a crummy power supply that goes out if you breathe on it, so it’s likely the camera is going temporarily offline during this time — but I’d like ffmpeg to wait out a timeout and resume streaming when it sees the camera again.