Review - Dahua IPC-HFW7442H-Z 4MP Ultra AI Varifocal Bullet Camera

[Wildcat_1]

All, thanks to Andy (@EMPIRETECANDY ) we have a new 4MP Ultra AI camera for testing. This camera has been provided by Andy in exchange for a fair and honest review.

This is a varifocal bullet cam with a 2.7mm to 12mm optical zoom and Ultra AI feature set. Full specs below and will be adding initial pics & vids in post 2, AI feature breakdown and testing in post 3, tips and tricks on how to make these features work and other settings / menu functions in post 4 and a summary of findings / recommendations in post 5. Trying this approach to the layout to make it easier to find for everyone going forward.

As there will be a ton of features to share with all of you as well as the standard image quality tests, once those are completed I may run comparison vids/pics (if interested) against some of the cams below with the B5442 being the obvious choice for comparison due to sensor performance.

• 5231 Pro (1/2.8” F1.6 2MP HD sensor)
• 5241 Pro AI (1/2.8” F1.5 2MP sensor)
• B5442 Pro AI (1/1.8” F1.8 4MP sensor)
• 2831 Lite (1/1.8” F1.9 8MP 4K sensor)
• 5831 Pro (1/2.5” F1.4 8MP 4K sensor)

I plan to review in multiple lighting conditions including extreme low light to test the Starlight+ features. Also intend to list some of the unique features of this particular camera not limited to its Ultra AI functionality but also FW / menu differences as well.

You will notice that in testing the cams I like to show not only the (in your face) quality tests but also some real world install tests in both close up and overview locations to push the camera and sensor as much as possible.

If there is anything anyone wants to see specifically, please let me know and will try to accommodate. Also be sure to check out @looney2ns review of the 8MP (4K) version of this cam here


Price from @Empirectecandy is approximately $480 and can be purchased here:
Aliexpress link - US $480.0 5% OFF|New Model IPC HFW7442H Z 4MP AI IR Bullet Network Camera, free DHL shipping-in Surveillance Cameras from Security & Protection on AliExpress - 11.11_Double 11_Singles' Day


Now onto the specs

Image Sensor - 1/1.8” 4Megapixel progressive CMOS - 2688 (H) × 1520 (V)​

ROM - 4 GB​

RAM - 2 GB​

Auto/Manual - 1/3 s–1/100000 s​

Min. Illumination - 0.001 Lux@F1.2 (Color, 1/3 s, 30 IRE)​

IR Distance - 50 m (164.04 ft)​

IR LEDs Number - 4 (IR LEDs)​

Focal Length - 2.7–12 mm​

Max. Aperture - F1.2​

​

Field of View​

Horizontal:111° (W)–47° (T)​

Vertical: 59° (W)–26° (T)​

Diagonal: 135° (W)–51° (T)​

​

Close Focus Distance​

W: 1 m (3.29ft)​

T: 2.5 m (8.20ft)​

AI Features

Perimeter Protection​

Tripwire, intrusion (Recognition of vehicle and people), fast moving, loitering detection, parking detection and people gathering.​

​

Face Detection​

Face detection, track, priority, snapshot, face enhancement, face exposure, face attribute extract, 6 attributes and 8 expressions: Gender, age, glasses, expressions (anger, sadness, disgust, fear, surprise, calm, happiness and confusion), mask, beard Face matting setting: face, single inch photo Three snapshot methods: real-time snapshot, priority snapshot, quality priority​

​

ANPR​

ANPR, track, priority, snapshot​

Vehicle attributes: license plate, vehicle type, vehicle color,vehicle brand​

Other attributes: sun visor, seat belt, smoking, phoning, in-car accessories, annual vehicle inspection mark​

​

Video Structurization​

Vehicle detection, non-motor vehicle detection, personnel detection, face detection, track, snapshot, optimization, report high quality picture, vehicle attributes (license plate, vehicle type, vehicle color, and vehicle brand), other attributes (sun visor, seatbelt, smoking, phoning, in-car accessories, and annual vehicle inspection mark), non-motor vehicle attributes: vehicle type, vehicle color,driver number, cloth type, cloth color, and hat), personnel attribute (cloth type, trousers type, cloth color, trousers color, knapsack, and gender), face attribute (gender, age , glasses, mask, and beard)​

​

People Counting​

People counting (count the entry number, exit number and pass number), display and output daily/monthly/annual statistics, No. in area​

Pics Of The Cam

BTW please excuse the install pics. As you can see I usually like clean installs in PVC or conduit but in the case of the test have to string a wire rather than make it look complete


Side View Of The Cam




Back View of The Cam
Showing Built In Connections For Junction Box - YES THATS A 3.5MM MIC INPUT, WAHOO !!!! I will be adapting one of my pro shotgun mics for further testing here on sound quality.




1st Install Location Pic
Showing Built In Junction Box Mounted Flush Horizontally




1st Install Location Pic 2
Showing 7442 (Bottom) Next to A 2831T




2nd Install Location
Next to a 5231-Z12 Showing Built In Junction Box Mounted Flush Vertically

 

[Wildcat_1]

Post # 2 - Reserved For Initial Pics & Vids

First Video - Initial Daytime Video + Associated AI Captures

This first daytime one focuses on a couple of real world install scenarios (overview, close up porch/entry) and I decided to edit together the vid caps with their AI pic captures. I’m using Video Structuralization as the Smart Plan and will go into more detail in the AI post over the coming days with how best to set this up (there are some tips and tricks for better AI success), ANPR and People captures along with differences between what you can accomplish in cam vs AI NVR.

Without going into too much detail in this post, Video Structuralization (VS) is really the Smart Plan you want to use with these AI cams. Sure you can setup ANPR or People detection separately but VS allows you to capture both without having to give up one PLUS also enables Face Detection as well. Therefore you are getting 3 AI features using 1 Smart Plan.

Will post Night next so you can see how this performs there as well. In the AI post I also put EVERY attribute to the test, if the cam says it can detect it, I try it . Enjoy !

Remember to change your Youtube settings.




Initial Night Video

All, here are some initial night video’s. Settings as stated and I purposefully did not dial the image in like I would normally as I wanted to go with bare minimum changes in these first videos. I neglected to say in the first video that lighting is 1 x 100w bulb on the porch, the side shots across the stop sign are lit by 1 LED street lamp (will grab an iPhone pic to show what it can see later). Will be completing more night time testing as well.

Also, as an ANPR / LPR individual (run 5231-Z12’s and 5241-Z12) I will be the first to tell you that the angle and height I mounted the cam at for this initial test is ABSOLUTELY NOT the way to position an ANPR / LPR BUT I wanted to show what is possible with this cam even in crazy, real life locations (punchline, I’m impressed)

Unfortunately as Youtube compresses the crap out of everything the videos usually take a bit of a beating so I built this from a ProRes proxy and uploaded that way which allowed a little more retention of detail etc

More to come in the AI post and I will be testing more locations, showing whats captured by the NVR in cases of the ANPR / Person Detection and what can be achieved when dialing in the image. Enjoy !

 

[Wildcat_1]

Post # 3 - Reserved For AI Feature Testing & Results

3.1 AI FEATURES - INITIAL ANPR

Now I couldn’t let all of you go into the weekend without giving you a taste of what everyone’s been waiting for……ANPR / LPR. Now as I mentioned, the first test (this one) was to really push the camera to the test, I would never setup an LPR camera on purpose at this angle or height but wanted to see what it could do in a regular overview position.

The way in which this camera works and what the end result is depends on where you are sending the feed.

On Cam (SD Card recording)

If you are only using on cam (SD card) then you will get :

• Separated video and pictures that are tagged CarDetect, assuming you are using Vehicle Detection under ANPR rule or my favorite Video Structuralization.
• As separate files do not link to each other when recorded in cam and would have to be viewed separately.
• Video does not have any overlay of the details of the capture, yet it does show on the web gui if watching live (the clips that pop up) and of course is burned into the picture.

You can configure the Vehicle Detection capture (in Video Structuralization) a number of ways but I configure so if it detects a plate then I end up with 3 pictures (full res wide shot with car details, plate close up and close view (vehicle and plate).

AI NVR

When using an AI NVR you get:

• Pictures and video’s together under Vehicle Detection in its AI menu
• Tags all plate info so you can search by it later, very cool !
• Due to last bullet item you can then search using the AI function for attributes such as Plate No., Color, Type, Logo, Plate Color, Ornament, Calling, Seatbelt.
• If you don't want to search by attributes then leave this completely open (ALL) and when you hit search it will pull back pic and video matches together !
• In conjunction with an AI NVR it also allows you to build Black & White Lists for plates

--
As video in cam does not overlay, lets take a first look at ANPR/LPR pics and cover AI later.

So, in this first example (with bad angle and height) at full zoom, how does the camera do…….in my opinion its VERY impressive but let me know your thoughts. I’ve obscured partial plates and faces to protect privacy. When you look at what its trying to capture from a PPI perspective, its amazing.

If you have the camera web gui up and Video Structuralization set as on-screen you get to see the results in real-time which also (in the case of the Ford) showed on screen that they were wearing seatbelts (unfortunately didn’t capture the screen grab in time).

DON’T WORRY MORE TO COME FOR ANPR/LPR AS WELL AS THE OTHER AI FEATURES

Couple of pics of vehicle capture
Each captured the plate accurately and when you look at the distance to target (70-80ft) its doing a great job based on a 2.7 to 12mm lens especially since the DORI rates this as just under 42ft (at full zoom like this) to identify and that would be a person not a plate




Video Structuralization Meta Clip From Web GUI




3.3 Night ANPR / LPR - Coming SOON

NEW 11/4

Ok, I know everyone wanted to see ANPR / LPR night captures + low light. Well, here is both in the same cap . Let me first call out some info:

• Snow was still an issue but made it work
• Cam at full zoom @ 1/500
• Sleet coming down with splash back from road made for tricky captures
• Still not ideal angle or height compared to my 5231 & 5241 LPR setups BUT the best I can do with this particular focal length for now as I wanted to show real-world installations rather than a simulation
• Camera moved to other location for ANPR / LPR which is completely dark except for some IR flashback due to the location
• NR turned off which introduced noise but with the sleet I wanted to try and combat any further loss in detail caused by NR
• EVERY vehicle captured here resulted in a full ANPR hit
• Editing was to obscure the plates for privacy
• The ANPR defaulted to a lot of info when this dark (such as color etc) although plate info was accurate
• Mix of clean and dirty plates to show what the camera is still able to do (check the E36 for example of a poor / tough situation where the cam still got a usable capture)
• Included slo-mo’s so you can look at each live frame then the associated capture
• Cam is applying its own processing (sharpening + enhancement) automatically to final image which although you don’t control (like you can in Face Detection), does a good job of pulling the plate out.

Enjoy !



----

As the snow kept coming down and I waited for the round 2 of the storm, I knew I couldn’t move the camera so instead got some further night shots which again, even at the bad height / angle accurately hit all ANPRs on the vehicles in these clips, EVEN WHEN IT WAS SNOWING ! It really does do an amazing job even in poor conditions.

I even gave you all some slo-mo video so you can go frame by frame. The original (non compressed for youtube) you can see much greater detail. I will be moving the camera to a more optimal, in your face location as soon as the temperature improves above 6 fahrenheit / -14 celsius.





3.4 People Detection - Attribute Testing

As I wait for the snow storm to stop so I can test more of the Night ANPR / LPR, I thought I would show you one of the other areas I committed to, testing this cam against all its stated People Attributes. Now as this was a warmer, sunnier day you can imagine what this must have looked like for others as they walked by BUT hey, wanted to test the cam. I also have another interesting twist on this test coming up which I will post as soon as I can.

So here’s how it did. It takes 2 pics under Human Detection AND if it can detect it (and you have enabled) can also grab Face Detection on top. This is why I love using Video Structuralization as it allows you to stack many AI features (definitely consider this tip !). In most case the AI did a great job except it could not detect the backpack, everything else worked.

 

[Wildcat_1]

Post # 4 - Reserved For Tips & Tricks, Dialing In The Cam
I mentioned I would share some tips as we go through so here is 1 that I wanted to share that made a big difference to ANPR capture for me. Hope these help !

Tip #1 - Configuration of Triggers For Video Structuralization Smart Plans - ANPR

Most here are used to setting up Detect Regions in IVS and normally its just the area you want to look at then your choice of recording that area (intrusion zoning) or when a specific tripwire is triggered. What I found in testing the Ultra AI series is there are some differences in the way this operates when using for things like ANPR. Specifically:

• Detection region triggers quite quickly (not normally a bad thing) which can cause the ANPR processing to kick in too early.
• The Ultra AI has an ‘Optimized Snap’ mode which although should use the camera AI to decide the best time to take the picture, in tougher or more tight environments (limited window for capture, angles etc) this often didn’t lead to the best results for final capture.
• The AI processing that delivers the ANPR capture details (metadata burned into the screen, shown in Video Structuralization Live View and sent to AI NVR's as well, if using them) takes place between the Detect Region & the Snap area (Optimized or Tripwire) and is automatic.

The above can lead to non optimal captures or missed ANPR opportunities. The solution is actually two fold:

1. If you look at the image below you can see that rather than Optimized Snap, I used Tripwire and on screen you can see where I drew that (diagonally across the stop sign). This gave much better results of plates than Optimized in this setup.
2. You’ll see the Detect Region on the road to the left of the image, this was before I adjusted it and after this screen cap I moved it to the right about halfway up that street (closer to the stop sign). This meant that the camera didn’t try and process too early, captured the vehicles in the sweet spot for this install location AND gave a narrower window for the AI cap and associated stills.

Tip #2 - Video Structuralization Is the Smart Plan for You Most of the Time……………Except When It Isn’t

As I’ve already mentioned in some of the posts above, Video Structuralization really does give you the best of *most* worlds when it comes to being able to capture and process most AI related video. Specifically it supports these 3 rules in parallel:

• Vehicle Detection (including ANPR details) + Stills
• Non-Vehicle Detection
• People Detection (including metadata) + Stills

BUT you also get a 4th thrown in as part of this:

• Face Detection (not to be confused with Face Recognition which this cam IPC-HFW7442H-Z does not support but the IPC-HFW7442H-ZFR does albeit that with that model you give up ANPR).

Video Structuralization works really well and you don’t have to compromise by picking a particular rule since you can capture 3 / 4 distinct rule types in this mode. However, there are some drawbacks (I have surfaced these to Andy for Dahua to take a look at):

1. You can only build 1 rule for each type such as 1 x People, 1 x Vehicle, 1 x Non vehicle which can be limiting when wanting to set multiple capture points
2. You can only draw 1 Detect Region that is then enabled (same one) for all 3 of the rules I mentioned in 1. In other words you cannot have 1 Detect Region for Vehicles, separate 1 for People etc, reported to Dahua and hope they unlock this one.
3. You are limited to 1 tripwire when using that for vehicle captures instead of Optimized Snap.

So you may be asking, well then why would I ever use anything else ? The answer to that is

a) if you fall into a category above when you want to have (at least today before any FW changes) multiple detect regions for a rule in which case standard IVS would be the way to go (remember no ANPR or People metadata there though) or for specific capture requirements (People Counting with reporting, Heat Maps, Face Recognition (with appropriate cam version)​

​

or​

​

b) ANPR with specific blacklist and whitelist needs.​

As I think most I mentioned above in a are self explanatory, lets focus on b, ANPR. Vehicle Detection (including ANPR metadata) is available in Video Structuraliation mode HOWEVER black and whitelisting of plates along with PTZ activation and alarm alerts from AI NVR’s ONLY currently work when the cam is set to Smart Plan = ANPR and can only be adjusted when and captured by the AI NVR when configured that way. Therefore if you must have ANPR black and whitelisting, alerts when certain plates are captured, PTZ activation on a specific plate etc then you would have to use ANPR as Smart Plan for now.

HTH

 

[Wildcat_1]

Summary / Recommendations / Final Thoughts

Well, I’ve now had this camera over a month and been putting it to use daily. I’ve used this both standalone and with an AI NVR (review coming of that soon as well) and so I wanted to sum up the camera from my perspective.

Overall Thoughts

At this point I think most of us are aware of the performance of the 1/1.8” sensors that Dahua is using and the really remarkable image that these are capable of in various cams (B&T variants of the 5442 etc). The 7442 takes that to the next level by combining these great sensors with an amazing SOC and additionally, Ultra AI.

So before we jump into AI, lets take a second to look at this as the bare cam itself and how it performs, in a word AMAZING. This camera certainly appears to be the ‘current’ perfect balance with regards to megapixels to available models to performance ratio. You get a bright, crisp, not overly processed image, sharp from edge to edge with enough resolution for most peoples uses. On the subject of available models there is also a Z4 variant which gives users the choice of extra focal length (8mm to 32mm) which works very well when used with AI features such as ANPR/LPR. There are also the ZFR variants which give you Face Recognition BUT you lost ANPR.

Why does SOC matter ? I touch on this a lot in my reviews but its something thats not talked about in general and that is SOC or System on Chip. Improvements there, combined with the RAM / ROM increases in the Pro & Ultra AI series make a huge difference to overall operating speed and performance of a cam. SOC and RAM/ROM may sound like a boring topic but trust me this is critical to these cams especially when pushing them to their limits which a number of people need to do.

When I say ‘makes a huge difference’, to what ? Well for example:

• Initial access time when logging into a cam
• Running multiple streams fluently
• Performing intensive simultaneous tasks (recording to SD, stream out to NVR, stream out to OpenALPR, AI tasks being performed)
• Reaction to trigger rules
• Reducing dropped frames
• Applying ROI to an image without stutter
• Ensuring captures are not missed
• etc etc

The 7442 does all of those in its stride and shines when doing it. Don’t get me wrong, the Pro AI series such as 5241-Z12, the B5442 (that I also reviewed) and the T5442 are no slouches either (in fact far from it, they fly) but the 7442 even makes them look like they’re taking a casual walk You can really get an appreciation for this when you are running rules like Video Structuralization with People, Vehicle & Non Vehicle rules all configured, watch an aspect of all 3 of these targets move into frame while in turn you are watching live in the Web GUI as it processes and displays metadata in real-time…….IMPRESSIVE !

AI

So lets tackle AI. The 7442 represents V2.0 of the Dahua AI framework and it does not look like they took this iteration lightly. Specifically the sheer amount of processing this camera is doing and more importantly able to then turn into meaningful results in real-time is phenomenal. Thats not to say it doesn’t have its quirks where at times you scratch your head (Optimized Capture for ANPR is one area, remember use Tripwire instead) wondering why the cam reacted at a certain point when there was a much better one in frame, but for the most part, it just works and works very well. A clear example of this is some of the night video I posted where you have water on the roads, snow coming down and no light on that part of the street and STILL the camera is able to process the image and return accurate ANPR results when a vehicle is driving 30mph + in those conditions. For a cam that costs significantly less at $480 you get one heck of a lot from it.

The AI is pretty much on point where, if you install correctly (height, angle, lighting etc) this camera never falters EXCEPT for backpacks, vehicle model sometimes being incorrect (could be part of this being an international version) and if we really want to nit pick, vehicle colors in IR mode (black, white or gray vehicles of course ). On the backpack front, don’t ask me why but any type of bag (a metadata component it should be able to detect) would never be picked up by this cam. Even if you don't have the perfect install, this cam still performs for you as you could see in some of the crazy angles I put it through.

Again, watching this cam pickup plate details from shots and speeds that you would have a hard time with the naked eye or processing metadata on a person and vehicle at 2 different points of the same frame is crazy good.

Bugs & Quirks

So, lets talk about bugs or quirks. After thoroughly testing this cam, I did find some bugs and some quirks. Thankfully these all appear to be FW related which means there is a high likelihood of these being fixed in the future. I’ve already reported the following back through Andy @EMPIRETECANDY for review by Dahua, thanks Andy ! Here’s the list I generated from my testing:

1. Only 1 People Counting, Vehicle, or Non Motor Vehicle rule allowed to be created in Web GUI.
   Should allow more than one rule per AI option
2. People Counting in web GUI does not allow you to setup more than 1 line AND does not let you set a direction for people flow.
   In the NVR it DOES allow you to change the flow (A to B or B to A) so this looks like a web GUI bug
3. When using Video Structuralization you can only have 1 Detect Region even if you use multiple rules such as Motor Vehicle, People, Non Motor Vehicle. Each rule should allow the creation of a different detect region as you may way to detect people in 1 area of the view, vehicles in another etc.
4. Video Structuralization missing some captures from a video tagging perspective.
   Specifically when using Vehicle triggers it can sometimes miss the video capture of a vehicle BUT still pictures capture every vehicle so this seems to be an issue with the IVS algorithms for Vehicle Detection in Video Structuralization which only happens on intermittent occasions.
5. Optimized Snap mode in Video Structuralization often captures vehicle at ‘wrong time’ often leads to plates being missed.
   Have to change to Tripwire mode to ensure a good capture. Would like to see additional tripwire addition option + ideally work on the Optimized snap algorithm.
6. Within Video Structuralization reports, ‘Daily’ only allows for solid 23 hours rather than 24 or at least 23hrs 59 minutes.
   Needs to be expanded to allow the extra 59 minutes to 1 hour
7. Backpacks & bags not being detected by camera. Needs algorithm tweaked to capture these correctly.

Setting expectations On Web GUI vs AI NVR Abilities For Certain Functions

Some people have asked me about what is vs isn’t possible with regards to ANPR, People Counting & other AI etc in web GUI vs AI NVR so I wanted to list them here. Its worth pointing out that anything that can be done in-cam CAN be done by the AI NVR (and more) but not the other way around:

In-Cam / Web GUI - Assume SD Card Installed For Local Capture

• ANPR / LPR Plate metadata is captured / embedded to the still images recorded when using ANPR or Video Structuralization with Vehicle Rule
• Vehicle Video Captures will NOT embed the metadata for the vehicle locally
• Web GUI can provide bar or line charts for # of Vehicle, Non-Vehicle, People it has captured across a period of time BUT cannot export the data behind it including metadata
• Cannot build black or whitelists within the web GUI

AI NVR

• All ANPR / LPR metadata is tagged, recorded and available for Smart Search by any of the metadata values (vehicle color, age of person, model)
• NVR playback of Vehicle Detection WILL also show the metadata while the video plays back
• Black & Whitelists can be built and saved to the AI NVR with ALARM OUT options when a plate is picked up matching one of the lists
• Exporting of Vehicle Detection data results in a wonderful CSV spreadsheet with hyperlinks to the embedded metadata still pics it also downloads at the same time, very cool !
• Exporting of any of the other AI data from the NVR does the same as above, spreadsheet with all details and hyperlinked local image.
• People counting report also gives you the opportunity to export CSV for hourly, daily counts OR pics across the timeframe specified
• When connecting an Ultra AI cam to an AI NVR, the 2 devices work in tandem from an AI perspective and in doing so enhance the AI capture experience and results

Wrap Up

So first a quick general observation. What is very clear is that this is an exciting time for cams from Dahua. We’re moving up gradually in the megapixel games for the right reasons (low light performance improving while not degrading image) and seeing some stand out cameras as you’ll see or have seen from other reviews on this forum. AI in V2.0 is really starting to become useful, V3.0 will only enhance this further. This means that for us users / installers / pro’s we have a great selection of cams, sensors and price points for every budget available to all of us on the forum and most importantly that these cams really do what they say on the tin. Here’s just a few of them below

• 2231T-ZS-S2 - Review
• 2431T-AS - Review
• 5241-Z12 - Replacement for the legendary 5231-Z12. Own both of these and they are great !
• T5442 - Review
• B5442 - Review
• 7442 - This Review
• 7842 - Review

So for what its worth, what is my personal recommendation on the 7442 ? If you are in the market for a 4MP vari bullet then you should ABSOLUTELY consider this cam. Yes you pay more for an Ultra series, Yes you pay more for AI BUT this AI actually works and really does add value in capture and review situations. Overall you are getting a ton of camera for not a lot of money in the grand scheme of price to performance.

If you don’t need metadata processing for ANPR / People etc or don’t need Face Recognition (the FR variants of this cam) then you can absolutely be safe in the decision to go with a B5442 / T5442 where you will still benefit from Pro AI for its Object Filtering of Human vs Vehicle (which also assists in review) and ensures objects you actually want captured are, while useless caps such as leaves blowing are not.

For me personally, I would pickup the 7442, 7442-Z4, in a heartbeat and have no problem recommending this camera (as long as you understand the quirks which hopefully will be fixed in FW and the situations where an AI NVR is needed or beneficial in relation to your capture requirements and workflow) for those looking to benefit from the AI functions it offers in conjunction with its solid sensor performance.

If there is one ask (have already asked Andy to take this to Dahua for consideration) it would be for a Z12 variant of this cam AND more models in the 7842 version

I hope you’ve enjoyed the testing and review of this cam. I will be starting another review shortly so look out for it. Hope this has helped anyone interested in the 7x42 Ultra AI series. As always, let me know with any questions.

Thanks for taking the time to follow along !

 

[Arjun]

Added to the AI Series Thread,

Dahua AI Series Thread

Cameras: Varifocal Lens Bullet (1). Dahua IPC-HFW7442H-Z 4MP Ultra AI Varifocal Bullet Camera / Reviewed by @Wildcat_1 https://ipcamtalk.com/threads/review-dahua-ipc-hfw7442h-z-4mp-ultra-ai-varifocal-bullet-camera.42642/ Network Video Recorders (NVR's): (1). Dahua NVR 4216-I / Reviewed by...

ipcamtalk.com

 

[morpheus]

Hello,

I am very interested in the Ultra-AI series and look forward to your review.

Could you answer a few questions?
According to the technical data, the Ultra-AI series can have four substreams instead of two.
1. can you say for which substreams mjpeg is possible as codec?

2. for which substreams is an image section available? (see picture)


3. can the image be rotated 90° at a resolution of 2688x1520 (full 4MP)? According to the technical data this is not possible, but I can't believe it. (see picture).



Many thanks in advance and many greetings
Morpheus

 

[Wildcat_1]

@morpheus

Answers to your questions

Q1 - Which sub streams (out of the 4) is MJPEG possible ?​

​

A - 1, Sub stream 1 allows for MJPEG only​

​

​

Q2 - Which sub streams is an image section available ?​

​

A - Sub stream 2 allows this​

​

​

Q3 - Can the image be rotated at 90 degrees @ 2688 x 1520 ?​

​

A - No, its not possible, will return an error of ‘Current resolution does not support this function), same for 270 degrees. 180 degrees however is allowed. If you want to flip to 90 degrees you have to drop to 1920 x 1080 (HD)​

 

[morpheus]

Thank you

 

[Arjun]

@Wildcat_1 How do you like the mounting system on this camera? Some users have some reservations over this

 

[Wildcat_1]

Ok everybody, first video is up in Post 2, yes, VIDEO (finally fixed the Youtube issue !!!!!).

 

[Wildcat_1]

@Wildcat_1 How do you like the mounting system on this camera? Some users have some reservations over this

@Arjun the mounting system isn't great but not necessarily due to the shape or design of the outer box but more the cable channel in it. In other words, mounting flush and cabling through the back is relatively straightforward, however when cabling through the main 'conduit' hole that becomes a little trickier again due to the way in which the mounting hole channels the cable.

Therefore due to the nature of the built in junction box there are a couple of things you need to be aware of:

1) The slot in which you feed your CAT cable is very tight. With that said CAT 5 & 6 cables do fit through BUT strain relief boots can cause a problem. Therefore if making the cable once you feed through the slot / channel you should be good, otherwise if you are using a cable you previously made (as I did in this test) then you'll want to pull the strain relief boot back.

2) Due to the almost 90 degree angle of where the cable feeds through the conduit / screw hole (unless installing through the back of the unit), run your cable through the cable slot first before mounting. If you try and pull a fully made (or pre-made) cable (which does fit minus the boot above) it will be much harder due to the aforementioned angle.

Also, be prepared to drill new holes for this mounting unit as the holes don't match previous junction boxes.

Certainly the mounting system is not a deal breaker in my opinion but certainly different and needs a little more thought when installing.

Hope that helps

 

[EMPIRETECANDY]

Wooo, thanks to @Wildcat_1 very professional testing , make this video costed him too much time and energy, the pic and AI showing is amazing in the daytime, let's wait his nightvision videos, and LPR functions showing.

 

[m00st]

Great review. Keen to read how the ANPR works and performs!

 

[tech101]

Interesting thank you waiting for night time video

 

[Wildcat_1]

Interesting thank you waiting for night time video

Initial night videos (10 mins worth) up in Post #2. More night time testing to come along with dialing in the image, further AI caps (showing off ANPR etc).

 

[bigredfish]

Good job man

 

[Davahad]

Looking to add another camera to count cars and get LPR from opposite direction. How does this store the count data? Is it just embedded into the video recording?

 

[Wildcat_1]

Looking to add another camera to count cars and get LPR from opposite direction. How does this store the count data? Is it just embedded into the video recording?

Metadata written back to / processed by the backend device i.e in cam or on NVR

 

[Davahad]

Can you show some reports of what the stats look like for the car counters? Interested in total # of cars for a day and if there is anymore detail (ie., hourly, etc,)