Cant run more than 3 OMXplayer Multiple Streams - raspberry-pi3

Using a raspberry pi I'm using omxplayer to play 3 screens of IP cameras. When I add the 4th, the screen goes blank and we have to start over.
GPU mem is set to 512. And log says that there is over 700mb GPU remaining.
So what do I do?

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what is the reason for my raspberry pi 3 camera stops working after few hours?

i am trying to take a long video that is week long with 6 raspberry pi 3 cameras. every camera is connected to electricity port with through a regular phone transformer that connects each 2 usb ports for the raspberries. i start the shooting using ssh from my computer and i can see the cameras log when each camera shoots a frame at the terminal log. the problem is that each time that i start a long run that is longer than several hours, some cameras deferentially just stop shooting, every time a different camera, and with no perticular reason. are there common reasons for cameras to stop shooting? maybe memory problem? or electricity problem?
would love to hear about a lead so i could check it.
thanks in advance

How to store image in the FPGA for real time video processing?

I am implementing a video processing project in real time which comes from an HDMI input. The video input is going to have a green background, which will be replaced by an image stored in the FPGA in order to generate a new video with a different background. I am using PYNQ-Z2 board.
So far, I have tried the following:
Storing the whole image in BRAM is not possible because there is not enough space
Using a second stream for the image and then try to mix the 2 streams (video + image). Cannot synchronize the 2 streams.
Store the image in RAM and use a double buffering scheme to load part of the image in BRAM. The first buffer is used for the processing 1 row of the image. The second one is used for loading the next row from DDR memory via the DMA (DMA is controlled by the CPU). When a row is done, then an interrupt is sent from the FPGA to the CPU so that the next line can be sent from DDR memory. Also, I switch the buffers so that new data starts loading. This solution has too much latency in the DMA transfer and the image in the video output is broken.

Apple watch series 3 or series 5 for development device?

I wish to start developing apps for the apple watch and I wonder if I can get just the series 3 model without cellular capability as the series 5 is a little too pricey for me. What am I missing if I don't get the series 5 and cellular capability?
Series 3 should be fine since the important part is that you'll have a physical device to test on. The Series 4 and Series 5 have rounded corners, more space on the display, and a bottom region outside of the safe area. Just make sure the 40mm and 44mm sizes are also tested in Simulator or SwiftUI previews.

Raspberry Pi 3 PiCamera Still Frame Rate

I'm working with a Raspberry Pi 3 that has a ribbon PiCamera. My problem is that I cannot get the still (not video) frame rate to be workable. In my application, the camera acts like a scanner using only a single row of each frame to watch things go by. While the concept is fine, what's killing me is the frame rate which I cannot get above 30 FPS.
A perfect solution would be for someone out there taking the raspicam source, stripped it down and tuning it for speed, and bolting it up to OpenCV. Has anyone done this? Did it work?
The Ava Group in Spain (https://www.uco.es/investiga/grupos/ava/node/40) took an initial stab at this, but their still frame rate is also limited to 30 FPS.

Optimizing the code using raspberry pi

I am running an image processing program on raspberry pi b+ which has only 300MB usable ram
This program basically follows a black line (line follower) and also detects traffic symbol using the same camera. This requires huge amount of ram and our raspberry pi is processing it slowly.
due to this i am facing problem in line following
What can i do to speed up the processing in the raspberry pi ???
Speed up an image processing program is a long story because there is a lot of way to do.
In your case that using Raspberry Pi B+, I have some recommends:
Software:
Convert all the image to binary before processing.
Cut all the not-need-to-process parts of image before processing (example: if you detect lane, just remove the upper part of image.
Using C++ is faster than Python (in case you are using OpenCV python library)
Camera:
Using CSI Camera (raspi-cam) will get you better image tranfer speed than USB webcam.
Hardware:
Overclock the CPU
If Raspberry Pi B+ is not mandatory, Raspberry Pi 2 is a better solution with more Ram.
One option would be to overclock your pi. To do this you would open the terminal (ctrl + t), then typing sudo raspi-config. Then press down until you have selected 'overclock', then press ENTER. press ENTER again to accept, and choose how much you would like to over clock. To exit menu, press ENTER to select the overclock, ENTER to confirm, then RIGHT, then RIGHT then ENTER to select 'Finish'

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