{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DUWKSSCBJD7IURTYTMAE6S44PG","short_pith_number":"pith:DUWKSSCB","schema_version":"1.0","canonical_sha256":"1d2ca9484148fe8a46789b004f4b9c79b1e2e528c069b95fd1529f33ac23f22c","source":{"kind":"arxiv","id":"2508.09650","version":1},"attestation_state":"computed","paper":{"title":"TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arbind Agrahari Baniya, Hao Xu, Mohamed Reda Bouadjenek, Richard Dazely, Sam Wells, Sunil Aryal","submitted_at":"2025-08-13T09:33:23Z","abstract_excerpt":"Robust ball tracking under occlusion remains a key challenge in sports video analysis, affecting tasks like event detection and officiating. We present TOTNet, a Temporal Occlusion Tracking Network that leverages 3D convolutions, visibility-weighted loss, and occlusion augmentation to improve performance under partial and full occlusions. Developed in collaboration with Paralympics Australia, TOTNet is designed for real-world sports analytics. We introduce TTA, a new occlusion-rich table tennis dataset collected from professional-level Paralympic matches, comprising 9,159 samples with 1,996 oc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.09650","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-13T09:33:23Z","cross_cats_sorted":[],"title_canon_sha256":"22245cb3493e1c0570d1feaeca9843c194516de7d7bc69201bbb19397e120203","abstract_canon_sha256":"aef7ef33f356acec4193dfa6db9b941b7e3dbb38c1e311211995392b8dff69eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:19.045172Z","signature_b64":"r9k3OSwM/iUg/nwiphO4DdJGmlIh9yweOjYANii3fM/y/tjwOzsBAAZpdE9V5ENf/9UBHK7DCpftGXrMvRagAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d2ca9484148fe8a46789b004f4b9c79b1e2e528c069b95fd1529f33ac23f22c","last_reissued_at":"2026-07-05T11:53:19.044687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:19.044687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arbind Agrahari Baniya, Hao Xu, Mohamed Reda Bouadjenek, Richard Dazely, Sam Wells, Sunil Aryal","submitted_at":"2025-08-13T09:33:23Z","abstract_excerpt":"Robust ball tracking under occlusion remains a key challenge in sports video analysis, affecting tasks like event detection and officiating. We present TOTNet, a Temporal Occlusion Tracking Network that leverages 3D convolutions, visibility-weighted loss, and occlusion augmentation to improve performance under partial and full occlusions. Developed in collaboration with Paralympics Australia, TOTNet is designed for real-world sports analytics. We introduce TTA, a new occlusion-rich table tennis dataset collected from professional-level Paralympic matches, comprising 9,159 samples with 1,996 oc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09650","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.09650/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.09650","created_at":"2026-07-05T11:53:19.044752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.09650v1","created_at":"2026-07-05T11:53:19.044752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09650","created_at":"2026-07-05T11:53:19.044752+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUWKSSCBJD7I","created_at":"2026-07-05T11:53:19.044752+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUWKSSCBJD7IURTY","created_at":"2026-07-05T11:53:19.044752+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUWKSSCB","created_at":"2026-07-05T11:53:19.044752+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09652","citing_title":"Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG","json":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG.json","graph_json":"https://pith.science/api/pith-number/DUWKSSCBJD7IURTYTMAE6S44PG/graph.json","events_json":"https://pith.science/api/pith-number/DUWKSSCBJD7IURTYTMAE6S44PG/events.json","paper":"https://pith.science/paper/DUWKSSCB"},"agent_actions":{"view_html":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG","download_json":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG.json","view_paper":"https://pith.science/paper/DUWKSSCB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.09650&json=true","fetch_graph":"https://pith.science/api/pith-number/DUWKSSCBJD7IURTYTMAE6S44PG/graph.json","fetch_events":"https://pith.science/api/pith-number/DUWKSSCBJD7IURTYTMAE6S44PG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG/action/storage_attestation","attest_author":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG/action/author_attestation","sign_citation":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG/action/citation_signature","submit_replication":"https://pith.science/pith/DUWKSSCBJD7IURTYTMAE6S44PG/action/replication_record"}},"created_at":"2026-07-05T11:53:19.044752+00:00","updated_at":"2026-07-05T11:53:19.044752+00:00"}