# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 8 Datasets
### Pedestrian Datasets  
Although vision-based approaches cannot collect the same level of information at night-day or low-light conditions, most of the detectors are based on colour images. This is due in part to the fact that many of datasets that are used for benchmarking are of colour images [106]. Some of the most commonly used datasets for evaluating pedestrian detection techniques are Caltech-USA [173], KITTI [104], ETH [174], TUD-Brussels [175]. These datasets are vision-based datasets [43]. Of these datasets, Caltech and KITTI are the major benchmarks for pedestrian detection as they are large datasets with pedestrians in scenes that pose challenges for detection due to crowded scenes, occlusion, etc. [25].  
The KAIST [142], a multispectral dataset, combines data collected using visible and thermal cameras. The KAIST dataset aims to improve the training of deep networks for detection, as most of the large-scale (i.e., Caltech, KITTI, etc.) use only RGB-based image. The issue with using only colour images is that it is assumed that the autonomous vehicle would be functioning in a well-lit condition, which is not always the case in practice. Therefore, thermal images can be used when the conditions do not allow visible images to function as designed. As KITTI has been so widely used, [142] was influenced to provide a similar quality dataset. They also used KITTI as a ground truth and valuation criteria.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Miss Rate** & **Dataset** \\ \hline HogLbp (HOG Local Binary Pattern) [34] & 49\% & Diamler \\ HOG [32] & 45\% & INRIA \\ JointDeep [155] & 45\% & ETH \\ HogLbp & 39\% & INRIA \\ MultiFtr+CSS & 39\% & Diamler \\ LatSVM-V2 [49] & 38\% & Diamler \\ MultiFtr & 36\% & INRIA \\ ConvNet (Convolutional Networks) [156] & 33\% & Diamler \\ MultiFtr+Motion & 29\% & Diamler \\ MLS (Macrofeature Layout Selection) [157] & 28\% & Diamler \\ WordChannels [158] & 16\% & INRIA \\ NAMC (Normalized Autobinomial Markov Channels) [159] & 15\% & INRIA \\ RandForest (Random Forest) [160] & 15\% & INRIA \\ SCCPriors (Symetric Cross-Channel Priors) [161] & 15\% & INRIA \\ Franken [162] & 14\% & INRIA \\ InformedHarr [163] & 14\% & INRIA \\ LDCF (Locally Decorrelated Channel Features) [61] & 14\% & INRIA \\ Roeret [164] & 14\% & INRIA \\ SketchTokens [165] & 13\% & INRIA \\ SpatialPooling [166] & 11\% & INRIA \\ RPN+BF (Region Proposal Network+Boosted Forest) [167] & 10\% & Caltech \\ MS-CNN (Multi-Scale CNN) [90] & 10\% & Caltech \\ SA-FastRCNN (Scale Aware-FastRCNN) [131] & 10\% & Caltech \\ UDN+U Unified Deep Networky [168] & 10\% & Caltech \\ Adaptive Faster R-CNN [169] & 9\% & Caltech \\ F-DNN+SS (Fused Deep Neural Network+Selective Search) [170] & 8\% & Caltech \\ F-DNN2+SS [170] & 8\% & Caltech \\ TLL-TFA (Topological Line Localization-Temporal Feature Aggregation) [171] & 7\% & Caltech \\ \hline \hline \end{tabular}  
* 3 Effectiveness of different methods evaluated on datasets for pedestrian detection. These results are from the Caltech Pedestrian Detection Benchmark and are arranged in a log-average miss rate (MR), where the lower the value, the more effective the method. The evaluation for the benchmark can be found in [11,172].  
\end{table}
Table 4: \(Cont\).