## 6 Traditional and DL Approaches for Object Detection in AVs
### Traditional Approaches for Object Detection in AVs
#### 6.1.2 Traditional Approaches for Pedestrian Detection  
This section delves into traditional techniques for pedestrian detection, comparing their effectiveness and limitations in various conditions. A notable study [115] presents a method that leverages an infrared camera for nighttime pedestrian detection, combining Haar-Cascade and Histogram of Oriented Gradients-Support Vector Machines (HOG-SVM) to reduce false alerts, demonstrating the superiority of this approach in low-light conditions. Another study [116] employed LiDAR point clouds and SVM for object clustering and classification in snowy environments, showcasing the robustness of this method for sidewalk snowplow machines at night. The real-world validation of this approach underscores its practical advantages over traditional vision-based systems in adverse weather. The work in [117] stands out for its real-time pedestrian detection system (PDS) that uses stereo-based segmentation and SVM to distinguish pedestrians from objects with vertical edge features. The high speed and detection rate of this method highlight its effectiveness, particularly in dynamic scenarios where camera motion is a factor. Research [118] addresses the challenge of detecting pedestrians from a moving vehicle in inclement weather, using SURF key points to compensate for camera motion and SVM for classification. The impressive performance of this approach, as evidenced by its speed and detection rate, underscores the potential of hybrid feature extraction methods in complex environments. Another study [119] combines Haar and HOG features to improve multi-person recognition and tracking, particularly in occluded and congested settings. The success of approaches in detecting both the side and frontal faces of pedestrians, as demonstrated through experiments on various datasets, highlights the limitations of single-feature approaches. The work in [120] introduces a novel method for pedestrian recognition in hazy environments by integrating a dark channel prior haze removal model with HOG descriptors. The improved performance of this method over conventional HOG-based techniques on the 'INRIA' dataset indicates the benefits of adaptive preprocessing for robust detection in challenging conditions. With an emphasis on various weather situations, the study in [121] presents a robust vision-based pedestrian detection system for intelligent transportation systems. It incorporates feature extraction and SVM-based classification and addresses issues like variability and occlusions in real traffic photos. In a more constrained environment, the work in [122] demonstrates a pedestrian recognition method using SVM and HOG fea  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Year** & **Technique** & **Weather Classes** & **Accuracy** \\ \hline [101] & 2014 & SVM+PHOW & Rain, Fog, Snow, Low light & 82.5\% \\ \hline [106] & 2022 & HOG–LBP & several weather & 96.4\% \\ \hline [108] & 2012 & histogram GDVM & Rain, Night & 92.4\% \\ \hline [111] & 2019 & tensor decomposition & Night & 95.82\% \\ \hline [113] & 2017 & Particle Filtering & Dust, Snow & 94.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vehicle detection previous work with traditional approaches.  
tures, achieving an impressive 98.00% accuracy in counting individuals in a limited field of view. This high accuracy, achieved through training on a dataset of 2000 road photos, underscores the effectiveness of the model in controlled settings, potentially outperforming other approaches that do not account for such precision. Another study [123] provides a two-phase video recording pedestrian recognition method that combines local and global data with HOG features. The system's efficacy in pedestrian detection is demonstrated by increased detection rates and decreased false positives obtained through testing with the PETS 2009 dataset.