Robust Rear-View Human Tracking for Robotic Visual Sensing: A Spatiotemporal Prediction and Multi-Modal Fusion Approach.

Xu Jia, Jia Xie, Yongguo Li, Jintao Liang, Zengmin Zhang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xu JiaSchool of Engineering, Shanghai Ocean University, Shanghai 201306, China.ORCID 0009-0008-2025-846X

Jia XieSchool of Engineering, Shanghai Ocean University, Shanghai 201306, China.

Yongguo LiSchool of Engineering, Shanghai Ocean University, Shanghai 201306, China.ORCID 0000-0001-6407-5763

Jintao LiangSchool of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China.ORCID 0000-0001-8165-3692

Zengmin ZhangSchool of Engineering, Shanghai Ocean University, Shanghai 201306, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we propose a lightweight tracking framework driven by spatiotemporal prediction and multimodal feature fusion. Specifically, an ego-motion-aware Kalman prediction mechanism maintains temporal continuity during complete occlusions. Upon target reappearance, a multi-factor descriptor-fusing color histograms with geometric constraints-is employed within a dynamic Mahalanobis search region. This is coupled with a specular-reflection-penalized adaptive learning rate (

Indexed as

adverse weatherKalman filtermultimodal fusionrear-view human trackingrobotic trackingspatiotemporal predictionvision sensors

Identifiers

PMID42122607

DOI10.3390/s26092884

PMCPMC13165865

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.