Nighttime FIR Pedestrian Detection Benchmark Dataset for ADAS

Abstract

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.