{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OJY6K52PFU7CAVP7AM6A7XVS25","short_pith_number":"pith:OJY6K52P","schema_version":"1.0","canonical_sha256":"7271e5774f2d3e2055ff033c0fdeb2d74a9d942ed2080e37a4b5fd742c1e3b56","source":{"kind":"arxiv","id":"2012.09393","version":2},"attestation_state":"computed","paper":{"title":"Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanghui Wang, Tianxiao Zhang, Xiaohan Zhang, Yiju Yang, Zongbo Wang","submitted_at":"2020-12-17T04:55:27Z","abstract_excerpt":"This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detection, including YOLO v3 and its tiny version, YOLO v4, Faster R-CNN, SSD, and RefineDet. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. At the tracking stage"},"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":"2012.09393","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-17T04:55:27Z","cross_cats_sorted":[],"title_canon_sha256":"d3179a38a0f3d539559613608dd73d30884b5dd4de845f1fc1b44736c007243b","abstract_canon_sha256":"102093cce7c7c86ebd97bbabf524aba71854922cf607746bf0ccd03d8721d50a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:34:10.580099Z","signature_b64":"yI8h7Q8Gdw350K/NPt2i6vZEe8StUS8j2QKOALFNgn7q19bF1DcazY5zMRVEyOq2jXjJT46W4rr/p35KaWJgCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7271e5774f2d3e2055ff033c0fdeb2d74a9d942ed2080e37a4b5fd742c1e3b56","last_reissued_at":"2026-07-05T02:34:10.579605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:34:10.579605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanghui Wang, Tianxiao Zhang, Xiaohan Zhang, Yiju Yang, Zongbo Wang","submitted_at":"2020-12-17T04:55:27Z","abstract_excerpt":"This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detection, including YOLO v3 and its tiny version, YOLO v4, Faster R-CNN, SSD, and RefineDet. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. At the tracking stage"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.09393","kind":"arxiv","version":2},"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/2012.09393/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":"2012.09393","created_at":"2026-07-05T02:34:10.579660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.09393v2","created_at":"2026-07-05T02:34:10.579660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.09393","created_at":"2026-07-05T02:34:10.579660+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJY6K52PFU7C","created_at":"2026-07-05T02:34:10.579660+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJY6K52PFU7CAVP7","created_at":"2026-07-05T02:34:10.579660+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJY6K52P","created_at":"2026-07-05T02:34:10.579660+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.21256","citing_title":"Tracking Moose using Aerial Object Detection","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25","json":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25.json","graph_json":"https://pith.science/api/pith-number/OJY6K52PFU7CAVP7AM6A7XVS25/graph.json","events_json":"https://pith.science/api/pith-number/OJY6K52PFU7CAVP7AM6A7XVS25/events.json","paper":"https://pith.science/paper/OJY6K52P"},"agent_actions":{"view_html":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25","download_json":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25.json","view_paper":"https://pith.science/paper/OJY6K52P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.09393&json=true","fetch_graph":"https://pith.science/api/pith-number/OJY6K52PFU7CAVP7AM6A7XVS25/graph.json","fetch_events":"https://pith.science/api/pith-number/OJY6K52PFU7CAVP7AM6A7XVS25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25/action/storage_attestation","attest_author":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25/action/author_attestation","sign_citation":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25/action/citation_signature","submit_replication":"https://pith.science/pith/OJY6K52PFU7CAVP7AM6A7XVS25/action/replication_record"}},"created_at":"2026-07-05T02:34:10.579660+00:00","updated_at":"2026-07-05T02:34:10.579660+00:00"}