面向群车协同感知的车载视频压缩算法

doi:10.11896/jsjkx.250400103

Abstract

Abstract: Collaborative vehicle crowdsensing significantly extends the perception range of individual cars,thereby greatly enhancing the safety of autonomous and assisted driving.However,it also faces the challenge of high transmission latency when dealing with high-precision,large-volume sensory data such as vehicle-mounted video.To solve this problem,the data transmission delay can be effectively reduced by removing redundant frames with invalid information from vehicle-mounted video.However,the dynamics and complexity of key information in vehicle-mounted video pose significant challenges in representing key and redundant information between frames and balancing the key information retention rate and compression rate.To solve the above challenges,this paper proposes a vehicle-mounted video compression algorithm for collaborative vehicle crowdsensing,aiming to balance information fidelity and compression efficiency.Specifically,it first employs target detection and multi-target tracking algorithms to extract continuous features of key information across video frames.Then,based on the low-rank property of video features,it converts the complex key and redundant information representation into a low-rank sparse matrix decomposition problem.Furthermore,it leverages the inexact augmented Lagrangian method to solve the problem.Finally,it evaluates the performance of the proposed algorithm using the real road dataset in Chongqing city and selected data from the public dataset BDD100K.Experimental results show that the proposed algorithm achieves an average 12.99% improvement in key information retention over four baseline methods under different traffic conditions,while reducing the transmission delay by 61.24% on average compared to the original video transmission.

Key words: Vedio compression, Collaborative vehicle crowdsensing, Low rank and sparse decomposition, Multiple targets tra-cking, Augmented Lagrangian method

CLC Number:

TP393

JIANG Zixian, YU Saixuan, HUANG Ruixue, SHEN Xin, HUANG Heqing. Vehicle-mounted Video Compression Algorithm for Collaborative Vehicle Crowdsensing[J].Computer Science, 2026, 53(4): 308-317.

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Vehicle-mounted Video Compression Algorithm for Collaborative Vehicle Crowdsensing

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