Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling

Abstract

<span lang="EN-US">The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: </span><em><span lang="EN-US">Institut National de Recherche en Sciences et Technologies du Numérique</span></em><span lang="EN-US"> (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples.</span>

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“…On the other hand, ResNet50 is a deep residual network that focuses on residual function learning, making it suitable for complex image recognition tasks [33]. The performance variation between MobileNet and ResNet50 in the context of PD classification aligns with research findings by Thu et al (2023), which showed that the pre-trained MobileNet outperformed ResNet50 in pedestrian classification [44].…”

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“…The pre-processed dataset will be a source for input into each CNN algorithm with various architectures, shown in Figure 4. There is the CNN algorithm with simple architectures [18], the CNN algorithm with a residual network-50 (ResNet50) architecture [19], [20], the CNN algorithm with a visual geometry group (VGG) 16 architecture [21], the CNN algorithm with convolutional neural networks for mobile vision applications (MobileNet) architecture [22] and CNN algorithm with an inception architecture [23]. Comparison graphically, the visualization CNN model architectures are simple architectures shown in Figure 4…”