## 5 Object Detection Application Domains
### Pedestrian Detection
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline
**Challenges** & **Method** \\ \hline \hline \multicolumn{3}{c}{Feature fusion [99]} \\ \multicolumn{3}{c}{Integral feature pyramid [37]} \\ \multicolumn{3}{c}{Topological line localization [100]} \\ \multicolumn{3}{c}{High-resolution handcrafted features [101; 102; 103]} \\ \multicolumn{3}{c}{To improve accuracy of small pedestrian detection} \\ \multicolumn{3}{c}{Ensemble detection [104]} \\ \multicolumn{3}{c}{Feature correlation layer [105]} \\ \multicolumn{3}{c}{Cascaded detection [106]} \\ \multicolumn{3}{c}{’Visual attention mechanism called as Region Context Network (RCN)’ [107]} \\ \hline \hline \multicolumn{3}{c}{'Ensemble of part detectors’ [108; 109]} \\ \multicolumn{3}{c}{’Guided attention mechanism’ [110]} \\ \multicolumn{3}{c}{To improve dense and occluded detection} \\ \multicolumn{3}{c}{Designing new loss function by considering both the attraction of target and repulsion of surrounding objects. [113]} \\ \hline \hline \multicolumn{3}{c}{By integration of ‘Boosted forest [100] and semantic segmentation’ [114]} \\ \multicolumn{3}{c}{’Bootstrap’ [41; 99; 115; 116]} \\ \multicolumn{3}{c}{’Anchor refinement module introduced in RefineDet’. [117]} \\ \multicolumn{3}{c}{Designing new loss functions [118; 40; 119].} \\ \multicolumn{3}{c}{’Cross-modal learning’ used to enrich the features of hard negatives using both RGB and infrared images [120]} \\ \end{tabular}
\end{table} 
Table 8: Remedies to improve challenges arising in pedestrian detection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Articles** & **Methods** & **Datasets** & **Remarks** \\ \hline [PERSON] et al. [108] & CNN & Caltech & Proposed Deep parts CNN detector is trained on weakly labelled data and can detect pedestrian by observing only a part of a proposal. \\ \hline \multirow{3}{*}{[PERSON] et al. [109]} & \multirow{3}{*}{Deep CNN} & \multirow{3}{*}{Caltech} & Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection uses deep CNN jointly learned’ \\  & & in order to maximize their strengths through cooperation. \\ \hline \multirow{3}{*}{[PERSON] et al. [121]} & \multirow{3}{*}{occlusion-aware R-CNN} & CityPersons, ETH, INRIA \\  & & & \\  & & and Caltech & prediction into the network to handle occlusion.\({}^{\prime}\) \\ \hline \multirow{2}{*}{[PERSON] et al. [122]} & \multirow{2}{*}{CNN} & Caltech and CityPersons \\  & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [123]} & multiscale block-based & \multirow{2}{*}{Caltech} & Method effectively processes images in which a crowd is present or pedestrians are partially occluded and enables pedestrian detection in images of different scenes. \\  & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [124]} & \multirow{2}{*}{Deformable Faster RCNN} & SORSI and HRRS & A deformable Faster R-CNN is constructed by substituting the standard convolution layer with a deformable convolution layer in the last network stage’ for occluded object detection. \\ \hline \multirow{2}{*}{[PERSON] et al. [125]} & \multirow{2}{*}{Enhanced Cascade detector} & \multirow{2}{*}{INRIA} & Proposed improved adaptive boosting (Adaboost) algorithm and enhanced cascade detector output to detect partially occluded pedestrians. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Various techniques to handle occlusion situations while detecting pedestrians.