{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DM7YIUARU4G7NPEHSTQCKDFHUI","short_pith_number":"pith:DM7YIUAR","schema_version":"1.0","canonical_sha256":"1b3f845011a70df6bc8794e0250ca7a23d2e6b8b704fb5ebf0fbc58833132517","source":{"kind":"arxiv","id":"1607.04564","version":3},"attestation_state":"computed","paper":{"title":"DAVE: A Unified Framework for Fast Vehicle Detection and Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Liu, Ling Shao, Matt Mellor, Yi Zhou","submitted_at":"2016-07-15T15:58:16Z","abstract_excerpt":"Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. In this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle's pose, color and type simultaneously. These two nets are "},"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":"1607.04564","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-15T15:58:16Z","cross_cats_sorted":[],"title_canon_sha256":"ef931a5dccbb081e6406bcb1010fea8f88d42fb0a03629f7b5d2012d34d570a6","abstract_canon_sha256":"8f97601228a066d1e67104c7a6abf60ce2380a06cc9ec8168a4f39115f77fb73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:14.111075Z","signature_b64":"cEnUOtb/huHokFBhBPoabvRaynfkO6GV/7t3FBe7y/YNrc0SYuof32NhkdwZDj/OkVzIX7q3wuwn+zyf30eMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b3f845011a70df6bc8794e0250ca7a23d2e6b8b704fb5ebf0fbc58833132517","last_reissued_at":"2026-05-18T01:10:14.110438Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:14.110438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DAVE: A Unified Framework for Fast Vehicle Detection and Annotation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Liu, Ling Shao, Matt Mellor, Yi Zhou","submitted_at":"2016-07-15T15:58:16Z","abstract_excerpt":"Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. In this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle's pose, color and type simultaneously. These two nets are "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04564","kind":"arxiv","version":3},"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":"1607.04564","created_at":"2026-05-18T01:10:14.110512+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.04564v3","created_at":"2026-05-18T01:10:14.110512+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04564","created_at":"2026-05-18T01:10:14.110512+00:00"},{"alias_kind":"pith_short_12","alias_value":"DM7YIUARU4G7","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DM7YIUARU4G7NPEH","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DM7YIUAR","created_at":"2026-05-18T12:30:12.583610+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/DM7YIUARU4G7NPEHSTQCKDFHUI","json":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI.json","graph_json":"https://pith.science/api/pith-number/DM7YIUARU4G7NPEHSTQCKDFHUI/graph.json","events_json":"https://pith.science/api/pith-number/DM7YIUARU4G7NPEHSTQCKDFHUI/events.json","paper":"https://pith.science/paper/DM7YIUAR"},"agent_actions":{"view_html":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI","download_json":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI.json","view_paper":"https://pith.science/paper/DM7YIUAR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.04564&json=true","fetch_graph":"https://pith.science/api/pith-number/DM7YIUARU4G7NPEHSTQCKDFHUI/graph.json","fetch_events":"https://pith.science/api/pith-number/DM7YIUARU4G7NPEHSTQCKDFHUI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI/action/storage_attestation","attest_author":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI/action/author_attestation","sign_citation":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI/action/citation_signature","submit_replication":"https://pith.science/pith/DM7YIUARU4G7NPEHSTQCKDFHUI/action/replication_record"}},"created_at":"2026-05-18T01:10:14.110512+00:00","updated_at":"2026-05-18T01:10:14.110512+00:00"}