{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QSFAJBLQBJ6F4A46IKLGXAXF2A","short_pith_number":"pith:QSFAJBLQ","schema_version":"1.0","canonical_sha256":"848a0485700a7c5e039e42966b82e5d032c74f137e4328fa76c18860af5a10c5","source":{"kind":"arxiv","id":"2409.05662","version":2},"attestation_state":"computed","paper":{"title":"Real-Time Human Action Recognition on Embedded Platforms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Carolyn M. Baum, Chenyang Lu, Jaehwan Jeong, Lisa Tabor Connor, Mingzhen Li, Peiqi Gao, Ruiqi Wang, Yejin Lee, Yihang Xu, Zichen Wang","submitted_at":"2024-09-09T14:35:23Z","abstract_excerpt":"With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline, running HAR on live video streams incurs excessive delays on embedded platforms. This work tackles the real-time performance challenges of HAR with four contributions: 1) an experimental study identifying a standard Optical Flow (OF) extraction technique as the latency bottleneck in a state-of-the-art HAR pipeline, 2) an exploration of the latency-accuracy tradeoff between the standard and deep learning approaches t"},"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":"2409.05662","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-09T14:35:23Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"7a3fb1222a8d1cc784b31b0db0e270e2f968b1fe7965034a38a22d1bec219d70","abstract_canon_sha256":"e1a40661ceaf986df82357b53269c68f30e06c302651c71d30ea876e3a1b824c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:35.673147Z","signature_b64":"HesV3Pow7bygdTLGU7sir6UEfYLEcTlCtJPHUEfaI6+y2M3pYZSOJikTJqSLC/gkAoNOOoIaspjjlPr/uMCOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"848a0485700a7c5e039e42966b82e5d032c74f137e4328fa76c18860af5a10c5","last_reissued_at":"2026-07-05T12:02:35.672363Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:35.672363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Real-Time Human Action Recognition on Embedded Platforms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Carolyn M. Baum, Chenyang Lu, Jaehwan Jeong, Lisa Tabor Connor, Mingzhen Li, Peiqi Gao, Ruiqi Wang, Yejin Lee, Yihang Xu, Zichen Wang","submitted_at":"2024-09-09T14:35:23Z","abstract_excerpt":"With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline, running HAR on live video streams incurs excessive delays on embedded platforms. This work tackles the real-time performance challenges of HAR with four contributions: 1) an experimental study identifying a standard Optical Flow (OF) extraction technique as the latency bottleneck in a state-of-the-art HAR pipeline, 2) an exploration of the latency-accuracy tradeoff between the standard and deep learning approaches t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.05662","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/2409.05662/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":"2409.05662","created_at":"2026-07-05T12:02:35.672457+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.05662v2","created_at":"2026-07-05T12:02:35.672457+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.05662","created_at":"2026-07-05T12:02:35.672457+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSFAJBLQBJ6F","created_at":"2026-07-05T12:02:35.672457+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSFAJBLQBJ6F4A46","created_at":"2026-07-05T12:02:35.672457+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSFAJBLQ","created_at":"2026-07-05T12:02:35.672457+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/QSFAJBLQBJ6F4A46IKLGXAXF2A","json":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A.json","graph_json":"https://pith.science/api/pith-number/QSFAJBLQBJ6F4A46IKLGXAXF2A/graph.json","events_json":"https://pith.science/api/pith-number/QSFAJBLQBJ6F4A46IKLGXAXF2A/events.json","paper":"https://pith.science/paper/QSFAJBLQ"},"agent_actions":{"view_html":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A","download_json":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A.json","view_paper":"https://pith.science/paper/QSFAJBLQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.05662&json=true","fetch_graph":"https://pith.science/api/pith-number/QSFAJBLQBJ6F4A46IKLGXAXF2A/graph.json","fetch_events":"https://pith.science/api/pith-number/QSFAJBLQBJ6F4A46IKLGXAXF2A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A/action/storage_attestation","attest_author":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A/action/author_attestation","sign_citation":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A/action/citation_signature","submit_replication":"https://pith.science/pith/QSFAJBLQBJ6F4A46IKLGXAXF2A/action/replication_record"}},"created_at":"2026-07-05T12:02:35.672457+00:00","updated_at":"2026-07-05T12:02:35.672457+00:00"}