Work

A Novel Study of Traffic Object Detection Based on Video Surveillance Streams

Year: 2026

Type: article

Abstract: Traditional traffic target detection heavily relies on manual processing. However, the latest advancements in deep learning have significantly enhanced the capabilities of target detection and multi-target tracking. To address these challenges, this paper proposes a perception-tracking-reasoning framework based on traffic rules, which is used for vehicle recognition and driving-state analysis in surveillance videos. This framework integrates enhanced vehicle perception, cross-frame identity association, trajectory-state modeling, and interpretable rule reasoning into a unified processing flow. Finally, experiments show that the main advantage of the proposed model lies in its ability to detect small-sized vehicle targets and improve trajectory stability in complex traffic scenarios.

Source: Applied Sciences

DOI: 10.3390/app16178541

Authors: Shujing Xie, Zhihao Zhang, Shuo Wang, Bowen Yang, Zhi Cai

Institutions: North China University of Water Resources and Electric Power, Beijing University of Technology

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896