We lifted 10 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| JonathonLuiten/TrackEval | pwc_unofficial | 4 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| create_coco_mask | Ran | JonathonLuiten/TrackEval/trackeval/baselines/baseline_utils.py code served (permissive licence) · get_code("e4a9dbe96206f1e6") |
| init_config | Ran | JonathonLuiten/TrackEval/trackeval/utils.py code served (permissive licence) · get_code("0ce058e665602da2") |
| threshold | Ran | JonathonLuiten/TrackEval/trackeval/baselines/baseline_utils.py code served (permissive licence) · get_code("407ecabd804b59d8") |
| update_config | Ran | JonathonLuiten/TrackEval/trackeval/utils.py code served (permissive licence) · get_code("5ef3f617efb9ddc4") |
| geometric_mean | Not yet run | JonathonLuiten/TrackEval/trackeval/plotting.py code served (permissive licence) · get_code("06d2be4ac9d0e95b") |
| jaccard | Not yet run | JonathonLuiten/TrackEval/trackeval/plotting.py code served (permissive licence) · get_code("00c662d6d9709fe4") |
| load_multiple_tracker_summaries | Not yet run | JonathonLuiten/TrackEval/trackeval/plotting.py code served (permissive licence) · get_code("e9f7b2829ca9731b") |
| load_seq | Not yet run | JonathonLuiten/TrackEval/trackeval/baselines/baseline_utils.py code served (permissive licence) · get_code("3aceddbf375bf009") |
| time | Not yet run | JonathonLuiten/TrackEval/trackeval/_timing.py code served (permissive licence) · get_code("df1bd31f4da1bed6") |
| validate_metrics_list | Not yet run | JonathonLuiten/TrackEval/trackeval/utils.py code served (permissive licence) · get_code("9792bf1150d36891") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, HOTA (Higher Order Tracking Accuracy), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2009.07736")
get_code_for_paper("2009.07736")
have("2009.07736")
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