{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DZUSC6OVANLEMISOHF66TQZEHO","short_pith_number":"pith:DZUSC6OV","canonical_record":{"source":{"id":"2412.05331","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T07:44:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a70fd916b9d8b24d209f0df5a0bf44778e407d1341667b984351acb796c90f18","abstract_canon_sha256":"2d98910a6a5861219d4ca2975736eeddeb6c3ebde5d8ba70dfe9ae7bba805efe"},"schema_version":"1.0"},"canonical_sha256":"1e692179d5035646224e397de9c3243bbf4ea593de9db5121dcfe93b0456a11d","source":{"kind":"arxiv","id":"2412.05331","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05331","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05331v3","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05331","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_12","alias_value":"DZUSC6OVANLE","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_16","alias_value":"DZUSC6OVANLEMISO","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_8","alias_value":"DZUSC6OV","created_at":"2026-07-05T10:15:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DZUSC6OVANLEMISOHF66TQZEHO","target":"record","payload":{"canonical_record":{"source":{"id":"2412.05331","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T07:44:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a70fd916b9d8b24d209f0df5a0bf44778e407d1341667b984351acb796c90f18","abstract_canon_sha256":"2d98910a6a5861219d4ca2975736eeddeb6c3ebde5d8ba70dfe9ae7bba805efe"},"schema_version":"1.0"},"canonical_sha256":"1e692179d5035646224e397de9c3243bbf4ea593de9db5121dcfe93b0456a11d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:23.652102Z","signature_b64":"G+2/ut3sJCatSKPMPh5vDW19oNzh/8v6ioS+8r8uKTQkk9nkv9IDqPRLtJPXhN8HsssotPBQEyBNT9ioP46sCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e692179d5035646224e397de9c3243bbf4ea593de9db5121dcfe93b0456a11d","last_reissued_at":"2026-07-05T10:15:23.651621Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:23.651621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.05331","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:15:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OxeTXRmKUGR4fdTkp6CCbA15jQFy4EhvzUHx2gVlWqkCDGMzcV2EfiuVIW52Q8DYmBiwWen8eZfhI90M5LAJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:48:27.407030Z"},"content_sha256":"33c9591c7786d5d398fa9685a98bb76476456c3cd31aba52fc5518dfdce9f0e2","schema_version":"1.0","event_id":"sha256:33c9591c7786d5d398fa9685a98bb76476456c3cd31aba52fc5518dfdce9f0e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DZUSC6OVANLEMISOHF66TQZEHO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shahran Rahman Alve","submitted_at":"2024-12-05T07:44:40Z","abstract_excerpt":"This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It utilizes the latest techniques in object detection and tracking, including Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, as well as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to achieve high accuracy in detection and captu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05331","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.05331/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:15:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+It1ZKTCtqHkqpbiRJycAMXRgTR/IcgNzAt8Szp90Os3NHzIWdraa87uh+5npisS4x6GfI62eiCq/N74RgXOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:48:27.407540Z"},"content_sha256":"2431e739be46118e1285a7173c158a6edac75c6d034054d9c3e2b48f032378a4","schema_version":"1.0","event_id":"sha256:2431e739be46118e1285a7173c158a6edac75c6d034054d9c3e2b48f032378a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DZUSC6OVANLEMISOHF66TQZEHO/bundle.json","state_url":"https://pith.science/pith/DZUSC6OVANLEMISOHF66TQZEHO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DZUSC6OVANLEMISOHF66TQZEHO/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":"2026-08-13T02:48:27Z","links":{"resolver":"https://pith.science/pith/DZUSC6OVANLEMISOHF66TQZEHO","bundle":"https://pith.science/pith/DZUSC6OVANLEMISOHF66TQZEHO/bundle.json","state":"https://pith.science/pith/DZUSC6OVANLEMISOHF66TQZEHO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DZUSC6OVANLEMISOHF66TQZEHO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DZUSC6OVANLEMISOHF66TQZEHO","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":"2d98910a6a5861219d4ca2975736eeddeb6c3ebde5d8ba70dfe9ae7bba805efe","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T07:44:40Z","title_canon_sha256":"a70fd916b9d8b24d209f0df5a0bf44778e407d1341667b984351acb796c90f18"},"schema_version":"1.0","source":{"id":"2412.05331","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.05331","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"arxiv_version","alias_value":"2412.05331v3","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05331","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_12","alias_value":"DZUSC6OVANLE","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_16","alias_value":"DZUSC6OVANLEMISO","created_at":"2026-07-05T10:15:23Z"},{"alias_kind":"pith_short_8","alias_value":"DZUSC6OV","created_at":"2026-07-05T10:15:23Z"}],"graph_snapshots":[{"event_id":"sha256:2431e739be46118e1285a7173c158a6edac75c6d034054d9c3e2b48f032378a4","target":"graph","created_at":"2026-07-05T10:15:23Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.05331/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It utilizes the latest techniques in object detection and tracking, including Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, as well as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to achieve high accuracy in detection and captu","authors_text":"Shahran Rahman Alve","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T07:44:40Z","title":"Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05331","kind":"arxiv","version":3},"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":"sha256:33c9591c7786d5d398fa9685a98bb76476456c3cd31aba52fc5518dfdce9f0e2","target":"record","created_at":"2026-07-05T10:15:23Z","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":"2d98910a6a5861219d4ca2975736eeddeb6c3ebde5d8ba70dfe9ae7bba805efe","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-05T07:44:40Z","title_canon_sha256":"a70fd916b9d8b24d209f0df5a0bf44778e407d1341667b984351acb796c90f18"},"schema_version":"1.0","source":{"id":"2412.05331","kind":"arxiv","version":3}},"canonical_sha256":"1e692179d5035646224e397de9c3243bbf4ea593de9db5121dcfe93b0456a11d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e692179d5035646224e397de9c3243bbf4ea593de9db5121dcfe93b0456a11d","first_computed_at":"2026-07-05T10:15:23.651621Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:15:23.651621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G+2/ut3sJCatSKPMPh5vDW19oNzh/8v6ioS+8r8uKTQkk9nkv9IDqPRLtJPXhN8HsssotPBQEyBNT9ioP46sCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:15:23.652102Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.05331","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33c9591c7786d5d398fa9685a98bb76476456c3cd31aba52fc5518dfdce9f0e2","sha256:2431e739be46118e1285a7173c158a6edac75c6d034054d9c3e2b48f032378a4"],"state_sha256":"6379ebeeffc31a1a1720629fdc9b56a8eac1308ac9bb79082251b108d06c50e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iOgdcBGgEOgTKW5k0KleUtkMoTnJFTNNtVbc06LS9ZYMjRgtF9Mbrq5VgC7GHa1V+5JDVBdYTJD1cYWZrT+bDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T02:48:27.413184Z","bundle_sha256":"20a40169ab3c62a99c4da735dab00a360f6b7b2e0e81c9becc0f023e6f3ad0d5"}}