YoloV5 Image identification and Tracking -How to Draw a A continuous line connecting the previous point and current point until the object is in frame

I am trying to Detect human objects and balls from video input, I am able to identify both objects and draw a square box around the identified objects, but how can I draw a continuous line in the trajectory in which they are moving? I have downloaded the detect.py file from YoloV5 Github Repo and customized the objects to identify.

I would like to draw a continuous line that connects the previous point and current point until the object is out of focus in the video?

I need to Draw a line on the balls Trajectory like in this image,

# Apply Classifier
if classify:
    pred = apply_classifier(pred, modelc, img, im0s)

# Process detections
for i, det in enumerate(pred):  # detections per image
    if webcam:  # batch_size >= 1
        p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
    else:
        p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

    p = Path(p)  # to Path
    save_path = str(save_dir / p.name)  # img.jpg
    txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
    s += '%gx%g ' % img.shape[2:]  # print string
    gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
    imc = im0.copy() if opt.save_crop else im0  # for opt.save_crop
    if len(det):
        # Rescale boxes from img_size to im0 size
        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

        # Print results
        for c in det[:, -1].unique():
            n = (det[:, -1] == c).sum()  # detections per class
            s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

        # Write results
        for *xyxy, conf, cls in reversed(det):
            if save_txt:  # Write to file
                xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                with open(txt_path + '.txt', 'a') as f:
                    f.write(('%g ' * len(line)).rstrip() % line + '\n')

            if save_img or opt.save_crop or view_img:  # Add bbox to image
                c = int(cls)  # integer class
                label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
                plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
                if opt.save_crop:
                    save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

    # Print time (inference + NMS)
    print(f'{s}Done. ({t2 - t1:.3f}s)')

    view_img=True
    # Stream results
    if view_img:
        cv2.imshow(str(p), im0)
        cv2.waitKey(1)  # 1 millisecond

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