Hi everyone
I'll keep this brief. Car has been swiped at some point on the front left side of the bumper. No idea when it has happened or if sentry mode caught it. I have hundreds of individual sentry mode folders when I download the data off the USB and I need to try and filter it somehow.
Three questions:
1. Each sentry mode recording has a "reason" in the metadata saved in the respective folder. Most repeated is "sentry_aware_object_detection". What are the other "sentry_aware*" categories? Eg. one for sentry mode events where the car has been nudged or hit whilst parked?
2. The metadata files are .json. If I've got hundreds, even if I get an answer to #1 above, is there a way to search within the files to only pull events which are, say, "sentry_aware_collision" (if that is even a thing).
3. Are there other filters that people have tried successfully when trawling back through their sentry data that might be useful in narrowing down the clips to focus on? I really don't want to trawl through hours of footage.
Thanks
Matt
I'll keep this brief. Car has been swiped at some point on the front left side of the bumper. No idea when it has happened or if sentry mode caught it. I have hundreds of individual sentry mode folders when I download the data off the USB and I need to try and filter it somehow.
Three questions:
1. Each sentry mode recording has a "reason" in the metadata saved in the respective folder. Most repeated is "sentry_aware_object_detection". What are the other "sentry_aware*" categories? Eg. one for sentry mode events where the car has been nudged or hit whilst parked?
2. The metadata files are .json. If I've got hundreds, even if I get an answer to #1 above, is there a way to search within the files to only pull events which are, say, "sentry_aware_collision" (if that is even a thing).
3. Are there other filters that people have tried successfully when trawling back through their sentry data that might be useful in narrowing down the clips to focus on? I really don't want to trawl through hours of footage.
Thanks
Matt