Nighttime FIR Pedestrian Detection Benchmark Dataset for ADAS
Far infrared(FIR)pedestrian detection is an essential module of the advanced driver assistance system(ADAS)at nighttime.Recently,a wave of deep convolutional neural networks(CNN)has taken the visible spectrum pedestrian detection benchmarks top ranks.However,due to the lack of dataset,we could not evaluate the performance of CNN methods on FIR images.In this paper,we introduce a nighttime FIR pedestrian dataset,which is the largest nighttime FIR pedestrian dataset.The dataset contains fine-grained annotated video,recorded from diverse road scenes and we provide detailed statistical analysis.We selected three kinds of advanced pedestrian detection methods as the baseline and evaluated their performance.Benefit from training data volume and diversity,the experimental results show that CNN-based detectors obtained good performance on FIR image.We also propose three suggestions for improving performance,which reduces the average miss rate of the vanilla Faster R-CNN by 12.97%and 9.77%on KAIST and our dataset respectively.The dataset will be public online.
FIR pedestrian detection Convolutional neural networks Dataset
Zhewei Xu Jiajun Zhuang Qiong Liu Jingkai Zhou Shaowu Peng
School of Software Engineering,South China University of Technology,Guangzhou 510006,China College of Computational Science,Zhongkai University of Agriculture and Engineering,Guangzhou 510225
国际会议
广州
英文
322-333
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）