{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:Z2UDDYSKRO2EAOW3E35OYYL3OJ","short_pith_number":"pith:Z2UDDYSK","schema_version":"1.0","canonical_sha256":"cea831e24a8bb4403adb26faec617b726f1c080f745079b50e62cdbcc37be437","source":{"kind":"arxiv","id":"1708.04006","version":1},"attestation_state":"computed","paper":{"title":"Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Zhou, Chunshui Zhao, Gang Hua, Jiaolong Yang","submitted_at":"2017-08-14T04:35:04Z","abstract_excerpt":"Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in "},"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":"1708.04006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-14T04:35:04Z","cross_cats_sorted":[],"title_canon_sha256":"a7dc5521e9afe3793cbe047bc05121d39e4dcac0d3056b5ef131f4593e84fce7","abstract_canon_sha256":"f9c10098e26797821e1989fff5ddefb4d02fdf88cf318c7f01844a65e321d6ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:07.287962Z","signature_b64":"AO2ffH9RucakB9pWD6hdDPN9UusfjejNX7xSztqNPY+JjVSa/JSIcRVZiyeet2/S8qsi8Xh8txevr9rwyxLJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cea831e24a8bb4403adb26faec617b726f1c080f745079b50e62cdbcc37be437","last_reissued_at":"2026-05-18T00:38:07.287239Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:07.287239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Zhou, Chunshui Zhao, Gang Hua, Jiaolong Yang","submitted_at":"2017-08-14T04:35:04Z","abstract_excerpt":"Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04006","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":""},"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":"1708.04006","created_at":"2026-05-18T00:38:07.287518+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.04006v1","created_at":"2026-05-18T00:38:07.287518+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04006","created_at":"2026-05-18T00:38:07.287518+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2UDDYSKRO2E","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2UDDYSKRO2EAOW3","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2UDDYSK","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ","json":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ.json","graph_json":"https://pith.science/api/pith-number/Z2UDDYSKRO2EAOW3E35OYYL3OJ/graph.json","events_json":"https://pith.science/api/pith-number/Z2UDDYSKRO2EAOW3E35OYYL3OJ/events.json","paper":"https://pith.science/paper/Z2UDDYSK"},"agent_actions":{"view_html":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ","download_json":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ.json","view_paper":"https://pith.science/paper/Z2UDDYSK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.04006&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2UDDYSKRO2EAOW3E35OYYL3OJ/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2UDDYSKRO2EAOW3E35OYYL3OJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ/action/storage_attestation","attest_author":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ/action/author_attestation","sign_citation":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ/action/citation_signature","submit_replication":"https://pith.science/pith/Z2UDDYSKRO2EAOW3E35OYYL3OJ/action/replication_record"}},"created_at":"2026-05-18T00:38:07.287518+00:00","updated_at":"2026-05-18T00:38:07.287518+00:00"}