{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:YMTTAY5UQ6W5TVKCUMAC5E6YSM","short_pith_number":"pith:YMTTAY5U","canonical_record":{"source":{"id":"1704.06326","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-20T20:24:50Z","cross_cats_sorted":[],"title_canon_sha256":"ec242231c67605e43fea3be0f186a4030dba75a62e6fac67ca0c635557b7f3d5","abstract_canon_sha256":"976f9318415ecea94145ac886c7d4744c83a834001b4d51e972426f00a3c7fda"},"schema_version":"1.0"},"canonical_sha256":"c3273063b487add9d542a3002e93d893036d6e65fc03cb47bce2a46e928cd0de","source":{"kind":"arxiv","id":"1704.06326","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06326","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06326v2","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06326","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"YMTTAY5UQ6W5","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YMTTAY5UQ6W5TVKC","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YMTTAY5U","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:YMTTAY5UQ6W5TVKCUMAC5E6YSM","target":"record","payload":{"canonical_record":{"source":{"id":"1704.06326","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-20T20:24:50Z","cross_cats_sorted":[],"title_canon_sha256":"ec242231c67605e43fea3be0f186a4030dba75a62e6fac67ca0c635557b7f3d5","abstract_canon_sha256":"976f9318415ecea94145ac886c7d4744c83a834001b4d51e972426f00a3c7fda"},"schema_version":"1.0"},"canonical_sha256":"c3273063b487add9d542a3002e93d893036d6e65fc03cb47bce2a46e928cd0de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:35.095144Z","signature_b64":"XvMmXteWnkn9L51ZdviynGAGPvThqZfTZBq87wh379/nxpqtsH829JjzTxMAt6E5i49rv7rUmpVzjjxWK7jFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3273063b487add9d542a3002e93d893036d6e65fc03cb47bce2a46e928cd0de","last_reissued_at":"2026-05-18T00:21:35.094558Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:35.094558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.06326","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B2DFsamCb2Eevfs8uBnPtO4Jx+t60EO4tVNLBrB4IaTbWR7OnhdvvaY48VNe0TBw7a49YUcKP+JMpfJM+SN6Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T09:07:03.111015Z"},"content_sha256":"cf987dbeb9c707eafd8be4ed96d79ac737874a1b84556ac168917390a8f49f4b","schema_version":"1.0","event_id":"sha256:cf987dbeb9c707eafd8be4ed96d79ac737874a1b84556ac168917390a8f49f4b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:YMTTAY5UQ6W5TVKCUMAC5E6YSM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Good Features to Correlate for Visual Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"A. Aydin Alatan, Erhan Gundogdu","submitted_at":"2017-04-20T20:24:50Z","abstract_excerpt":"During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual trackin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06326","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PN8BhOEiOXgT9gnabqywCzaf6ovutrQibsPQglBCitjTKIUs2jq944OQwLm8IUOYw0SWeU0GdwTOcd9kiKunBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T09:07:03.111376Z"},"content_sha256":"e687ca170448d173677dd7a05b52c9b87f2e924e221d9b642f6cc5ccae25967b","schema_version":"1.0","event_id":"sha256:e687ca170448d173677dd7a05b52c9b87f2e924e221d9b642f6cc5ccae25967b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/bundle.json","state_url":"https://pith.science/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/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":"2026-06-05T09:07:03Z","links":{"resolver":"https://pith.science/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM","bundle":"https://pith.science/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/bundle.json","state":"https://pith.science/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YMTTAY5UQ6W5TVKCUMAC5E6YSM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:YMTTAY5UQ6W5TVKCUMAC5E6YSM","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":"976f9318415ecea94145ac886c7d4744c83a834001b4d51e972426f00a3c7fda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-20T20:24:50Z","title_canon_sha256":"ec242231c67605e43fea3be0f186a4030dba75a62e6fac67ca0c635557b7f3d5"},"schema_version":"1.0","source":{"id":"1704.06326","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06326","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06326v2","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06326","created_at":"2026-05-18T00:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"YMTTAY5UQ6W5","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"YMTTAY5UQ6W5TVKC","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"YMTTAY5U","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:e687ca170448d173677dd7a05b52c9b87f2e924e221d9b642f6cc5ccae25967b","target":"graph","created_at":"2026-05-18T00:21:35Z","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":"During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual trackin","authors_text":"A. Aydin Alatan, Erhan Gundogdu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-20T20:24:50Z","title":"Good Features to Correlate for Visual Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06326","kind":"arxiv","version":2},"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:cf987dbeb9c707eafd8be4ed96d79ac737874a1b84556ac168917390a8f49f4b","target":"record","created_at":"2026-05-18T00:21:35Z","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":"976f9318415ecea94145ac886c7d4744c83a834001b4d51e972426f00a3c7fda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-20T20:24:50Z","title_canon_sha256":"ec242231c67605e43fea3be0f186a4030dba75a62e6fac67ca0c635557b7f3d5"},"schema_version":"1.0","source":{"id":"1704.06326","kind":"arxiv","version":2}},"canonical_sha256":"c3273063b487add9d542a3002e93d893036d6e65fc03cb47bce2a46e928cd0de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3273063b487add9d542a3002e93d893036d6e65fc03cb47bce2a46e928cd0de","first_computed_at":"2026-05-18T00:21:35.094558Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:35.094558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XvMmXteWnkn9L51ZdviynGAGPvThqZfTZBq87wh379/nxpqtsH829JjzTxMAt6E5i49rv7rUmpVzjjxWK7jFCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:35.095144Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.06326","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf987dbeb9c707eafd8be4ed96d79ac737874a1b84556ac168917390a8f49f4b","sha256:e687ca170448d173677dd7a05b52c9b87f2e924e221d9b642f6cc5ccae25967b"],"state_sha256":"d473825b52c2fab34e1c6bb97e26e2cf7641bc1cae88203503707e12938f973c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tRc6w/1wgs4ay4ujxR4yZIxMv1hRMzjaJijiY22U6F6WkB8/blAGScLuq5XrCLRLuiiVVhu4ZWDxzBYyLXOnBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T09:07:03.113288Z","bundle_sha256":"9efb0bcaaec02afcee53a0870e32658d668d23e1e812b0813580eb0fbae2c74f"}}