{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DIJHULGH2ERZP5EJRSPII4SHVI","short_pith_number":"pith:DIJHULGH","schema_version":"1.0","canonical_sha256":"1a127a2cc7d12397f4898c9e847247aa1b4d80ab3a5a7940e13b0f0ea0bbc993","source":{"kind":"arxiv","id":"2104.08859","version":1},"attestation_state":"computed","paper":{"title":"Filtering Empty Camera Trap Images in Embedded Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eulanda M. dos Santos, Fagner Cunha, Juan G. Colonna, Raimundo Barreto","submitted_at":"2021-04-18T13:56:22Z","abstract_excerpt":"Monitoring wildlife through camera traps produces a massive amount of images, whose a significant portion does not contain animals, being later discarded. Embedding deep learning models to identify animals and filter these images directly in those devices brings advantages such as savings in the storage and transmission of data, usually resource-constrained in this type of equipment. In this work, we present a comparative study on animal recognition models to analyze the trade-off between precision and inference latency on edge devices. To accomplish this objective, we investigate classifiers "},"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":"2104.08859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-04-18T13:56:22Z","cross_cats_sorted":[],"title_canon_sha256":"180ceba58544fb9c5fe122a5b103d320318eb8bead6867045ddadf593b7bc8e2","abstract_canon_sha256":"cd908928410f5c23feb481f365f166490174821d21a8af2703f5c8205bf63b4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:32:56.278496Z","signature_b64":"V4hl/qBgkB4hYZAM/UttKW03bgGo3txmIh5xxRqTlf6gZZx64ulkCjRbcQgL7szVL/FRe1/jZMd2SV4hYpDUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a127a2cc7d12397f4898c9e847247aa1b4d80ab3a5a7940e13b0f0ea0bbc993","last_reissued_at":"2026-07-05T02:32:56.278136Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:32:56.278136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Filtering Empty Camera Trap Images in Embedded Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eulanda M. dos Santos, Fagner Cunha, Juan G. Colonna, Raimundo Barreto","submitted_at":"2021-04-18T13:56:22Z","abstract_excerpt":"Monitoring wildlife through camera traps produces a massive amount of images, whose a significant portion does not contain animals, being later discarded. Embedding deep learning models to identify animals and filter these images directly in those devices brings advantages such as savings in the storage and transmission of data, usually resource-constrained in this type of equipment. In this work, we present a comparative study on animal recognition models to analyze the trade-off between precision and inference latency on edge devices. To accomplish this objective, we investigate classifiers "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.08859","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2104.08859/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":"2104.08859","created_at":"2026-07-05T02:32:56.278192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.08859v1","created_at":"2026-07-05T02:32:56.278192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.08859","created_at":"2026-07-05T02:32:56.278192+00:00"},{"alias_kind":"pith_short_12","alias_value":"DIJHULGH2ERZ","created_at":"2026-07-05T02:32:56.278192+00:00"},{"alias_kind":"pith_short_16","alias_value":"DIJHULGH2ERZP5EJ","created_at":"2026-07-05T02:32:56.278192+00:00"},{"alias_kind":"pith_short_8","alias_value":"DIJHULGH","created_at":"2026-07-05T02:32:56.278192+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/DIJHULGH2ERZP5EJRSPII4SHVI","json":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI.json","graph_json":"https://pith.science/api/pith-number/DIJHULGH2ERZP5EJRSPII4SHVI/graph.json","events_json":"https://pith.science/api/pith-number/DIJHULGH2ERZP5EJRSPII4SHVI/events.json","paper":"https://pith.science/paper/DIJHULGH"},"agent_actions":{"view_html":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI","download_json":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI.json","view_paper":"https://pith.science/paper/DIJHULGH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.08859&json=true","fetch_graph":"https://pith.science/api/pith-number/DIJHULGH2ERZP5EJRSPII4SHVI/graph.json","fetch_events":"https://pith.science/api/pith-number/DIJHULGH2ERZP5EJRSPII4SHVI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI/action/storage_attestation","attest_author":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI/action/author_attestation","sign_citation":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI/action/citation_signature","submit_replication":"https://pith.science/pith/DIJHULGH2ERZP5EJRSPII4SHVI/action/replication_record"}},"created_at":"2026-07-05T02:32:56.278192+00:00","updated_at":"2026-07-05T02:32:56.278192+00:00"}