I've built several object detection systems and you definitely want to fine-tune your model to get good results, even if the object class you want to track is a

I've built several object detection systems and you definitely want to fine-tune your model to get good results, even if the object class you want to track is available in a pre-trained model (often it isn't). For example, pre-trained models don't work too well at detecting people in surveillance footage when the camera is mounted on the ceiling and people are at an angle in the footage. Off-the-shelf object detection models also don't work well in poor lighting conditions but can work well if you fine-tune one of the pre-trained models for your dataset and lighting conditions. Here's a great and fun tutorial on how to fine-tune an object detection model using TensorFlow (it's a surprisingly fiddly process but worth the headache): https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9 https://towardsdatascience.com/how-to-train-your-own-object-... I suspect you can use your fine-tuned model with this repo by replacing the `get_ssd_model.sh` compiled graph with your fine-tuned model graph. It's also a fun exercise to implement the tracking yourself - here's a solid tutorial to get you started: https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/ https://www.pyimagesearch.com/2018/08/13/opencv-people-count...