object recognition issue

In Object Recognition mode there is no way to differentiate various different objects of the same category, eg: person, bike, etc. Additionally, in its initial mode, all the 20 objects recognized do NOT have representative Object IDs assigned and all return Object ID=0.

To remedy the problem, you need to train minimum a SINGLE object for each of the 20 categories and assigned them unique Object IDs 1-20. eg:

aeroplane=1, bicycle=2, bird=3, boat=4, bottle=5, bus=6, car=7, cat=8, chair=9, cow=10, diningtable=11, dog=12, horse=13, motorbike=14, person=15, pottedplant=16, sheep=17, sofa=18, train=19, TV=20.

Alternatively, you can assign totally arbitrary numbers to each category, as long as each one is unique and not shared with any other.

Once that is done, any type of object detected in this mode will return a BLOCK Info data that has the category Object ID as the last field. Keep in mind, the object training image you use  for the category has nothing to do with the actual detection and only serves the purpose of assigning an Object ID to the category. You need to make sure you pick an image that is of the category type and is identified ass the name of the category in the the detection process, ie: do NOT train a bicycle category with the picture of a tree !!!

Looking the number up and matching to a table you create will tell you what type of object you have detected. 

Once you are happy with the results, you can save the Object Recognition data to the SD card and your Object IDs will be preserved for any future use by simply reloading that training data for the algorithm category.

You can use additional attributes provided by the HuskyLens library such as position (x and y coordinates), size (width and height), and other characteristics to differentiate between the objects.