{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:G6ZZMEFTLXNSD5ET2D5246AECW","short_pith_number":"pith:G6ZZMEFT","canonical_record":{"source":{"id":"1311.1939","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.CV","submitted_at":"2013-11-08T11:29:15Z","cross_cats_sorted":[],"title_canon_sha256":"6e08f72579dbb05f8e154c24da107c5fbad30507d111d4f88d506dc8003f86f3","abstract_canon_sha256":"b45d5aad9f1de3282e43db50bb38de8d155e494988d4b6ea6d1968372fb7a1eb"},"schema_version":"1.0"},"canonical_sha256":"37b39610b35ddb21f493d0fbae780415b97aea5e3d22ab106339135ec21cb9c2","source":{"kind":"arxiv","id":"1311.1939","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.1939","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"1311.1939v1","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.1939","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"G6ZZMEFTLXNS","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_16","alias_value":"G6ZZMEFTLXNSD5ET","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_8","alias_value":"G6ZZMEFT","created_at":"2026-05-18T12:27:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:G6ZZMEFTLXNSD5ET2D5246AECW","target":"record","payload":{"canonical_record":{"source":{"id":"1311.1939","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.CV","submitted_at":"2013-11-08T11:29:15Z","cross_cats_sorted":[],"title_canon_sha256":"6e08f72579dbb05f8e154c24da107c5fbad30507d111d4f88d506dc8003f86f3","abstract_canon_sha256":"b45d5aad9f1de3282e43db50bb38de8d155e494988d4b6ea6d1968372fb7a1eb"},"schema_version":"1.0"},"canonical_sha256":"37b39610b35ddb21f493d0fbae780415b97aea5e3d22ab106339135ec21cb9c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:07:41.936776Z","signature_b64":"G+sVC/7AdotLyggcWSkVJ7Xn2z/opKPzMkaGaX0y+xrkrShmFvZpQT5fWwfUyQMK7lpUPo54lxzNXJGZEYfUCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37b39610b35ddb21f493d0fbae780415b97aea5e3d22ab106339135ec21cb9c2","last_reissued_at":"2026-05-18T03:07:41.936036Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:07:41.936036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1311.1939","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+TOaCKW5McFrmy3wTvxfG17vc9n4istqS5NqrON5YaDfaeOpXH9ckGKNa3Vk5BfgyH7v1TMS4PDLu++4TZDzAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:12:40.083148Z"},"content_sha256":"14493bd8d1927baa2b1b0344715193005a5ca40d6dce4e05ba66234a2c4c7055","schema_version":"1.0","event_id":"sha256:14493bd8d1927baa2b1b0344715193005a5ca40d6dce4e05ba66234a2c4c7055"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:G6ZZMEFTLXNSD5ET2D5246AECW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Tracking via Spatio-Temporal Context Learning","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David Zhang, Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang","submitted_at":"2013-11-08T11:29:15Z","abstract_excerpt":"In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Tran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.1939","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xo3Qg+WCygMDbNDahfMZIfQfFygS7nGhkHqfXpWDXuglmgi5DxnfUWvhiSTWBuodJgU6SO1NG2cRdkNlEo5oDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T17:12:40.083521Z"},"content_sha256":"ca498e87fd563040f10ffb0fe05948ac72195110b96525b0b4154dac6128cac8","schema_version":"1.0","event_id":"sha256:ca498e87fd563040f10ffb0fe05948ac72195110b96525b0b4154dac6128cac8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G6ZZMEFTLXNSD5ET2D5246AECW/bundle.json","state_url":"https://pith.science/pith/G6ZZMEFTLXNSD5ET2D5246AECW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G6ZZMEFTLXNSD5ET2D5246AECW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T17:12:40Z","links":{"resolver":"https://pith.science/pith/G6ZZMEFTLXNSD5ET2D5246AECW","bundle":"https://pith.science/pith/G6ZZMEFTLXNSD5ET2D5246AECW/bundle.json","state":"https://pith.science/pith/G6ZZMEFTLXNSD5ET2D5246AECW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G6ZZMEFTLXNSD5ET2D5246AECW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:G6ZZMEFTLXNSD5ET2D5246AECW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b45d5aad9f1de3282e43db50bb38de8d155e494988d4b6ea6d1968372fb7a1eb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.CV","submitted_at":"2013-11-08T11:29:15Z","title_canon_sha256":"6e08f72579dbb05f8e154c24da107c5fbad30507d111d4f88d506dc8003f86f3"},"schema_version":"1.0","source":{"id":"1311.1939","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.1939","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"1311.1939v1","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.1939","created_at":"2026-05-18T03:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"G6ZZMEFTLXNS","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_16","alias_value":"G6ZZMEFTLXNSD5ET","created_at":"2026-05-18T12:27:45Z"},{"alias_kind":"pith_short_8","alias_value":"G6ZZMEFT","created_at":"2026-05-18T12:27:45Z"}],"graph_snapshots":[{"event_id":"sha256:ca498e87fd563040f10ffb0fe05948ac72195110b96525b0b4154dac6128cac8","target":"graph","created_at":"2026-05-18T03:07:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Tran","authors_text":"David Zhang, Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.CV","submitted_at":"2013-11-08T11:29:15Z","title":"Fast Tracking via Spatio-Temporal Context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.1939","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:14493bd8d1927baa2b1b0344715193005a5ca40d6dce4e05ba66234a2c4c7055","target":"record","created_at":"2026-05-18T03:07:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b45d5aad9f1de3282e43db50bb38de8d155e494988d4b6ea6d1968372fb7a1eb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.CV","submitted_at":"2013-11-08T11:29:15Z","title_canon_sha256":"6e08f72579dbb05f8e154c24da107c5fbad30507d111d4f88d506dc8003f86f3"},"schema_version":"1.0","source":{"id":"1311.1939","kind":"arxiv","version":1}},"canonical_sha256":"37b39610b35ddb21f493d0fbae780415b97aea5e3d22ab106339135ec21cb9c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37b39610b35ddb21f493d0fbae780415b97aea5e3d22ab106339135ec21cb9c2","first_computed_at":"2026-05-18T03:07:41.936036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:07:41.936036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G+sVC/7AdotLyggcWSkVJ7Xn2z/opKPzMkaGaX0y+xrkrShmFvZpQT5fWwfUyQMK7lpUPo54lxzNXJGZEYfUCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:07:41.936776Z","signed_message":"canonical_sha256_bytes"},"source_id":"1311.1939","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14493bd8d1927baa2b1b0344715193005a5ca40d6dce4e05ba66234a2c4c7055","sha256:ca498e87fd563040f10ffb0fe05948ac72195110b96525b0b4154dac6128cac8"],"state_sha256":"933157e215c7ebc4da0a04319ed2a38f25d63dc48af2d33bc3b45823ce897426"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ByWz6y1caMalYU9x1BaOvI1uNxkUyHIlWKk7GoPQWx6j16i7pcbP3f+vBLoxxKqgToCSAUl0BRgrXiQYMRFXAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T17:12:40.085723Z","bundle_sha256":"18a919fd8a359e10468a70fe83ab80a43ce16c109d4763747e6ed4ff6da4db3c"}}