How to increase accuracy of Object Detection and mainly Tracking Computer Vision application of AI to industrial deployment scale?

Question

I'm currently using coco-ssd mobilenet from tensorflow.js. I'm using this model to basically predict bounding boxes around sports ball which is present in coco-dataset. But the accuracy is quite low. I'm basically working on object detection and tracking by making an association between both using the centroid-tracking algorithm in javascript. Everything works great but when the default model cannot predict ball for n number of frames, the tracker discards that ball and assumes it a new one. So, I want to increase the accuracy so that my detection does not fail.

This is how I am importing coco-ssd model from tensorflow

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js" type="text/javascript"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></script>

And this is how I am loading the model to further predict bounding boxes

cocoSsd.load({ base: "mobilenet_v2" }).then(function(loadedModel) {
    console.log("Loaded Model");
    model = loadedModel;
});

Making inference, here video is the actual video element of html.

model.detect(video).then(function(predictions) { 
     console.log(predictions)
});

So, how do I improve the object tracking with loosing detections. Any suggestions or ideas!