thanksalot for Telraam and this community! My sensor/system is now running for around four weeks now and it finally noticed some heavy vehicles, which is great!
Bikes and two-wheelers still have the largest share in my Telraam’s data which doesn’t match my subjective perception at this time of year (e.g. when there really large crowds of people wandering around on Christmas, it merely matched the daily average of counts).
Is there a chance, the device is counting pedestrians or cars as two-wheelers? If so, is there a chance this will change? Thanksalot for any help!
First of all: Welcome to Telraam Talks! Secondly: Good question, I’m sure @Telraam can answer your question in more detail, but the post below might already give you an idea of potential inaccuracies in Telraam counts.
Good question! I’ll try and explain it how Telraam counts and classifies the counted objects as pedestrian / cyclist / car or heavy vehicle.
The initial classification happens between pedestrians vs. two-wheelers vs; vehicles (=cars + heavy vehicles) based on two parameters: fullness value and axis ratio
Fullness means how well this red shaded area fills up the space inside the black rectangle.
Axisratio is the ratio of the two arrows.
After this, there is a classification between cars and heavy vehicles within the class “vehicles”. This is done based on a cut-off value. However, a calibration (period) is needed so that this cut-off value is optimally calculated for your location.
So your conclusion (larger groups pedestrians or two-wheelers) could be right. If there is a large group of pedestrians walking by and the fullness and axisration looks more like the parameters of a vehicle, it will be counted as a vehicle. Same thing for larger two-wheelers or groups of two-wheelers.
Does this help understanding why your counting data are what they are?
Unfortunately the counting algorithm will stay the way it is and errors like this won’t be solved. We are thinking however of making something so Telraam owners can put remarks on their dashboard (for certain days) to explain some numbers, so other people who are looking at the data know to interpret (certain) data with the right background information.