A Multi-Label Classification Method for Vehicle Video

In the last few years,smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents,which causes many scholars to pay attention to autonomous driving.For this complexity scene,one of the major challenges is mining information comprehensively from massive features in vehicle video.This paper proposes a multi-label classification method MCM-VV(Multi-label Classification Method for Vehicle Video)for vehicle video to judge the label of road condition for unmanned system.Method MCM-VV includes a process of feature extraction and a process of multi-label classification.During feature extraction,grayscale,lane line and the edge of main object are extracted after video preprocessing.During the multi-label classification,the algorithm DR-ML-KNN(Multi-label K-nearest Neighbor Classification Algorithm based on Dimensionality Reduction)learns the training set to obtain multi-label classifier,then predicts the label of road condition according to maximum a posteriori principle,finally outputs labels and adds the new instance to training set for the optimization of classifier.Experimental results on five vehicle video datasets show that the method MCM-VV is effective and efficient.The DR-ML-KNN algorithm reduces the runtime by 50%.It also reduces the time complexity and improves the accuracy.

Near-Optimal Transmission Optimization of Cache-Based SVC Vehicle Video

A problem of video streaming of scalable video coding(SVC)is studied in vehicular networks.To improve the performance of the video streaming services and alleviate the pressure on backhaul links,the small cell base stations(SBS)is proposed with caching ability to assist the content delivery.In this paper,it introduced the problem of joint optimization of caching strategy and transmission path in SBS cache cluster.An integer programming problem was formulated to maximize the average quality of experience.In order to obtain the globally optimal solution,the primal problem was first relaxed,then an adaptive algorithm was used based on the joint KKT condition,and the branch definition algorithm was applied.Extensive simulations were performed to demonstrate the efficiency of our proposed caching strategy.

Road boundary estimation to improve vehicle detection and tracking in UAV video

Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.