{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QT2SDEY3SK7I3XBXOLP3DB3FYB","short_pith_number":"pith:QT2SDEY3","schema_version":"1.0","canonical_sha256":"84f521931b92be8ddc3772dfb18765c0540e212017283c7e407a36f1fad6da29","source":{"kind":"arxiv","id":"1609.03056","version":2},"attestation_state":"computed","paper":{"title":"Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tiejun Huang, Yaowei Wang, Yemin Shi, Yonghong Tian","submitted_at":"2016-09-10T14:24:38Z","abstract_excerpt":"Learning the spatial-temporal representation of motion information is crucial to human action recognition. Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion. To address this problem, this paper proposes a long-term motion descriptor called sequential Deep Trajectory Descriptor (sDTD). Specifically, we project dense trajectories into two-dimensional planes, and subsequently a CNN-RNN network is employed to learn an effective representation for long-term motion. Unlike the popular two-stream ConvNets, the sDT"},"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":"1609.03056","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-10T14:24:38Z","cross_cats_sorted":[],"title_canon_sha256":"4f937eb30a8a51887e833188e173e1283a8ad9156359f5475db29b29aa8b2af8","abstract_canon_sha256":"02f6952b7c68c923a3e18a163fae37956527ae831e5d26f56a818003430c4663"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:01.153578Z","signature_b64":"L0v5MtPvkVebvt587PPljduwtExNvOUcGQQYfK8BZupsfc6OozqPvukHXKswvN642HSJ3gHTtmc2mnbY5ADUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84f521931b92be8ddc3772dfb18765c0540e212017283c7e407a36f1fad6da29","last_reissued_at":"2026-05-18T00:51:01.153191Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:01.153191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Tiejun Huang, Yaowei Wang, Yemin Shi, Yonghong Tian","submitted_at":"2016-09-10T14:24:38Z","abstract_excerpt":"Learning the spatial-temporal representation of motion information is crucial to human action recognition. Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion. To address this problem, this paper proposes a long-term motion descriptor called sequential Deep Trajectory Descriptor (sDTD). Specifically, we project dense trajectories into two-dimensional planes, and subsequently a CNN-RNN network is employed to learn an effective representation for long-term motion. Unlike the popular two-stream ConvNets, the sDT"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.03056","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":""},"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":"1609.03056","created_at":"2026-05-18T00:51:01.153246+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.03056v2","created_at":"2026-05-18T00:51:01.153246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.03056","created_at":"2026-05-18T00:51:01.153246+00:00"},{"alias_kind":"pith_short_12","alias_value":"QT2SDEY3SK7I","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"QT2SDEY3SK7I3XBX","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"QT2SDEY3","created_at":"2026-05-18T12:30:41.710351+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/QT2SDEY3SK7I3XBXOLP3DB3FYB","json":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB.json","graph_json":"https://pith.science/api/pith-number/QT2SDEY3SK7I3XBXOLP3DB3FYB/graph.json","events_json":"https://pith.science/api/pith-number/QT2SDEY3SK7I3XBXOLP3DB3FYB/events.json","paper":"https://pith.science/paper/QT2SDEY3"},"agent_actions":{"view_html":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB","download_json":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB.json","view_paper":"https://pith.science/paper/QT2SDEY3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.03056&json=true","fetch_graph":"https://pith.science/api/pith-number/QT2SDEY3SK7I3XBXOLP3DB3FYB/graph.json","fetch_events":"https://pith.science/api/pith-number/QT2SDEY3SK7I3XBXOLP3DB3FYB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB/action/storage_attestation","attest_author":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB/action/author_attestation","sign_citation":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB/action/citation_signature","submit_replication":"https://pith.science/pith/QT2SDEY3SK7I3XBXOLP3DB3FYB/action/replication_record"}},"created_at":"2026-05-18T00:51:01.153246+00:00","updated_at":"2026-05-18T00:51:01.153246+00:00"}