Pith Number

pith:P3RUC76I

pith:2026:P3RUC76IZZKFN7KK33NUB4DK54

not attested not anchored not stored refs resolved

Local Spatiotemporal Convolutional Network for Robust Gait Recognition

Cunrong Li, Wu Wang, Xiaoyun Wang

A dual-branch network endows standard 2D convolutions with the ability to extract temporal gait motion patterns.

arxiv:2605.14548 v1 · 2026-05-14 · cs.CV

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

we propose a Local Spatiotemporal Convolutional Network (LSTCN), a structurally simple yet highly effective dual-branch architecture that endows standard two-dimensional convolutional networks with the capacity to extract temporal information.

C2weakest assumption

That reducing gait tensors via horizontal and vertical strip-based local representations (GBSP) allows standard 2D convolutions to effectively capture intrinsic motion patterns without significant loss of discriminative information under covariate changes.

C3one line summary

LSTCN is a dual-branch CNN that extracts temporal gait features by pooling spatial data into strips and applying local spatiotemporal convolutions with asymmetric kernels.

References

52 extracted · 52 resolved · 0 Pith anchors

[1] Overview of biometrics research, 2021

[2] Deep gait recog- nition: A survey, 2023

[3] Few could be better than all: Feature sampling and grouping for scene text detection, 2022

[4] Optimal boxes: Boosting end-to-end scene text recogni- tion by adjusting annotated bounding boxes via reinforce- ment learning, 2022

[5] A com- prehensive study on cross-view gait based human identi- fication with deep CNNs, 2017

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:05.741166Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7ee3417fc8ce5456fd4adedb40f06aef3e898ef663251ef44f79c735230db679

Aliases

arxiv: 2605.14548 · arxiv_version: 2605.14548v1 · doi: 10.48550/arxiv.2605.14548 · pith_short_12: P3RUC76IZZKF · pith_short_16: P3RUC76IZZKFN7KK · pith_short_8: P3RUC76I

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P3RUC76IZZKFN7KK33NUB4DK54 \
  | 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: 7ee3417fc8ce5456fd4adedb40f06aef3e898ef663251ef44f79c735230db679

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "47cbce086b576a85f70e08084d00b117bff6b5b9cea4d064de3332a0c5480a9a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T08:28:49Z",
    "title_canon_sha256": "5ca38fcfa6e99ce49f5440e68a24c0950a85654271c0dddcbb8e45dbd38233b8"
  },
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  "source": {
    "id": "2605.14548",
    "kind": "arxiv",
    "version": 1
  }
}