Pith Number

pith:4FRWERRY

pith:2026:4FRWERRYJFDTTKL6ILZ24KR4LZ

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MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes

Daniele Palossi, Francesco Conti, Luca Benini, Luca Bompani, Manuele Rusci

MR2-ByteTrack enables video object detection with up to 55% energy savings on microcontroller-based vision sensors by alternating resolutions and rescoring detections.

arxiv:2605.15423 v1 · 2026-05-14 · cs.CV · cs.AI · eess.IV

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

70 extracted · 70 resolved · 7 Pith anchors

[1] S. C. Mukhopadhyay, S. K. S. Tyagi, N. K. Suryadevara, V . Piuri, F. Scotti, and S. Zeadally, ‘‘Artificial intelligence-based sensors for next generation iot applications: A review,’’IEEE Sensors Jour 2021

[2] W. Su, L. Li, F. Liu, M. He, and X. Liang, ‘‘Ai on the edge: a comprehensive review,’’Artif. Intell. Rev., vol. 55, no. 8, p. 6125–6183, Dec. 2022. [Online]. Available: https://doi.org/10.1007/s10462- 2022 · doi:10.1007/s10462-022-10141-4

[3] W. Y u, F. Liang, X. He, W. G. Hatcher, C. Lu, J. Lin, and X. Y ang, ‘‘A survey on the edge computing for the internet of things,’’IEEE Access, vol. 6, pp. 6900–6919, 2018 2018

[4] K. S. Patle, R. Saini, A. Kumar, and V . S. Palaparthy, ‘‘Field evaluation of smart sensor system for plant disease prediction using lstm network,’’ IEEE Sensors Journal, vol. 22, no. 4, pp. 3715–3725 2022

[5] A. Sabato, S. Dabetwar, N. N. Kulkarni, and G. Fortino, ‘‘Noncontact sensing techniques for ai-aided structural health monitoring: A systematic review,’’IEEE Sensors Journal, vol. 23, no. 5, pp. 4672– 2023

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-20T00:00:57.844623Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e163624638494739a97e42f3ae2a3c5e5bee0c6a099dde5c501a5eb1c80ffb92

Aliases

arxiv: 2605.15423 · arxiv_version: 2605.15423v1 · doi: 10.48550/arxiv.2605.15423 · pith_short_12: 4FRWERRYJFDT · pith_short_16: 4FRWERRYJFDTTKL6 · pith_short_8: 4FRWERRY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4FRWERRYJFDTTKL6ILZ24KR4LZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e163624638494739a97e42f3ae2a3c5e5bee0c6a099dde5c501a5eb1c80ffb92

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b41e1ac4e50b9c18a4b516f21ea057eae9c978954984f211bf6a769a5358c633",
    "cross_cats_sorted": [
      "cs.AI",
      "eess.IV"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T21:13:56Z",
    "title_canon_sha256": "9a8b912843987b034c5ca861ddec9bbecc940db33e27dafd3079589d9008adf4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15423",
    "kind": "arxiv",
    "version": 1
  }
}