Unfortunately you can't convert the complete YOLOv3 model to a tensorflow lite model at the moment. This is because YOLOv3 extends on the original darknet backend used by YOLO and YOLOv2 by introducing some extra layers (also referred to as YOLOv3 head portion), which doesn't seem to be handled correctly (atleast in keras) in preparing the model for tflite conversion.
You can convert YOLOv3 to .tflite without the model's 'head' portion (See here: https://github.com/benjamintanweihao/YOLOv3), but then you will have to implement the missing parts in your Java code (as suggested here: https://github.com/wics1224/yolov3-android-tflite). Make sure you have the correct anchor box sizes if you do so. The second link would hopefully answer the second part of your question.
If you plan to keep things simple, your other options would be using SSD-mobilenet or yolov2-tiny for your application. They will give you a more real-time experience.
I am currently working on a similar project involving object detection in flutter/tflite so I'll keep you updated if I find anything new.
Edit:
In https://github.com/benjamintanweihao/YOLOv3, you'll need to change how you import libraries because lite library is moved out from contrib from tensorflow 1.14 onwards.