{"paper":{"title":"MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"MR2-ByteTrack enables video object detection with up to 55% energy savings on microcontroller-based vision sensors by alternating resolutions and rescoring detections.","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Daniele Palossi, Francesco Conti, Luca Benini, Luca Bompani, Manuele Rusci","submitted_at":"2026-05-14T21:13:56Z","abstract_excerpt":"Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, and privacy constraints. However, these sensory systems typically rely on ultra-low-power microcontrollers (MCUs) with limited memory and compute, making conventional video object detection methods, which require feature storage or multi-frame buffering, unfeasible. To address this challenge, we introduce Multi-Resolution Rescored ByteTrack (MR2-ByteTrack), a Video Object Detection (VOD) method tailored for MCU-based embedded vision nodes. MR2-Byt"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our method yields up to 55% energy savings compared to processing only full-resolution images, enabling the first real-time Transformer-based VOD on an MCU-class embedded vision node.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The Rescore algorithm, which applies probability union rules to aggregate detection confidence scores across frames, can reliably correct misclassifications from low-resolution inferences without degrading overall performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MR2-ByteTrack maintains high accuracy in video object detection on MCUs by combining multi-resolution processing, ByteTrack for frame linking, and Rescore for confidence aggregation, achieving up to 55% energy savings and real-time performance for both CNN and Transformer models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MR2-ByteTrack enables video object detection with up to 55% energy savings on microcontroller-based vision sensors by alternating resolutions and rescoring detections.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2180dee21ad4a78081f79ac03e737ebfab3b0c4aa68bb52c8ee46d54dc925f64"},"source":{"id":"2605.15423","kind":"arxiv","version":1},"verdict":{"id":"d1fb2793-35f1-42dc-a929-722e7972751c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:20:15.327680Z","strongest_claim":"our method yields up to 55% energy savings compared to processing only full-resolution images, enabling the first real-time Transformer-based VOD on an MCU-class embedded vision node.","one_line_summary":"MR2-ByteTrack maintains high accuracy in video object detection on MCUs by combining multi-resolution processing, ByteTrack for frame linking, and Rescore for confidence aggregation, achieving up to 55% energy savings and real-time performance for both CNN and Transformer models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The Rescore algorithm, which applies probability union rules to aggregate detection confidence scores across frames, can reliably correct misclassifications from low-resolution inferences without degrading overall performance.","pith_extraction_headline":"MR2-ByteTrack enables video object detection with up to 55% energy savings on microcontroller-based vision sensors by alternating resolutions and rescoring detections."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15423/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T16:21:58.911783Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T15:50:40.754606Z","status":"completed","version":"0.1.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T15:31:17.785083Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T15:30:42.762899Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.139672Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.701953Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"dc27aab67f5779495d37343b22b2db1cd84271c81e276179a306c6e055cdb9a3"},"references":{"count":70,"sample":[{"doi":"","year":2021,"title":"S. 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Fortino, ‘‘Noncontact sensing techniques for ai-aided structural health monitoring: A systematic review,’’IEEE Sensors Journal, vol. 23, no. 5, pp. 4672–","work_id":"b7740639-464a-4ad2-8e56-992b276b9230","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":70,"snapshot_sha256":"7ba3bd85a010425eb1b8c9d8d73464aeb01d85e22bd23eb46a9e5c50ea838de7","internal_anchors":7},"formal_canon":{"evidence_count":1,"snapshot_sha256":"e2c1c1298ab6d1159b1d591e913997ca449f043b96af8393d1df7bdd8dcc02bb"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}