Bounding Box Interpolation for Temporal Tracking

Overview

In video-based detection, helmet/object bounding boxes are often missing in some frames due to occlusion or detector failure. Rather than dropping those frames, collect known bbox coordinates into a DataFrame indexed by frame number, insert NaN rows for missing frames, and call interpolate(limit_direction='both'). This produces smooth trajectories for cropping player-centered patches across a temporal window.

Quick Start

import numpy as np
import pandas as pd

def interpolate_bboxes(detections, frame_range, subsample=4):
    """Interpolate missing bboxes across a temporal window.
    detections: DataFrame with columns [frame, left, width, top, height]
    frame_range: (start_frame, end_frame) inclusive
    subsample: take every Nth frame after interpolation
    """
    det_indexed = detections.set_index('frame')[['left', 'width', 'top', 'height']]
    bboxes = []
    for f in range(frame_range[0], frame_range[1] + 1):
        if f in det_indexed.index:
            bboxes.append(det_indexed.loc[f].values)
        else:
            bboxes.append([np.nan] * 4)

    bboxes = pd.DataFrame(bboxes, columns=['left', 'width', 'top', 'height'])
    bboxes = bboxes.interpolate(limit_direction='both').values
    return bboxes[::subsample]

bboxes = interpolate_bboxes(frame_dets, (frame - 24, frame + 24), subsample=4)

Workflow

1. Query detected bounding boxes within the temporal window around the target frame
2. Build a list with known coordinates or NaN for missing frames
3. Convert to DataFrame and call interpolate(limit_direction='both')
4. Subsample (e.g., every 4th frame) to reduce channel count
5. Use interpolated coordinates to crop fixed-size patches from each frame

Key Decisions

• limit_direction='both': fills NaNs at both ends (forward + backward), critical when early/late frames are missing
• Subsample rate: every 4th frame balances temporal coverage vs. channel count
• Group by player: average multiple detections per frame when tracking pairs or groups
• vs optical flow: interpolation is simpler and sufficient when bbox drift between frames is small

References

• NFL 2.5D CNN Baseline [Inference]
• [Training] NFL 2.5D CNN (LB:0.671 with TTA)