# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 1 Introduction  
Pedestrian attribute recognition has become popular because of its enormous application scope. It refers to recognizing the attributes of a pedestrian from a given image, i.e., gender, age, upper body dress, lower body dress, etc., as shown in Figure 1. A surveillance scenario might contain multiple pedestrians. Recognizing all the pedestrians with their attributes from a surveillance image is a very challenging task [1]. This attribute recognition task is mainly helpful for person retrieval, person re-identification, person search, etc. Machine learning and deep learning techniques are now required for automation jobs since they outperform conventional methods.  
For image processing, deep learning algorithms are utilized instead of the previously prevalent handmade approaches. The primary reason for that is a deep learning-based model can automatically extract features, whereas handmade methods require user intervention. Deep neural network (DNN)-based models for image processing applications may be pretty sophisticated. There are a lot of parameters in these DNN models. Because of increases in computational power, image identification can now be made in real-time, but getting better results remains a problem. The convolutional neural network (CNN) is an essential approach for extracting characteristics from images. Deep CNN has a great learning ability due to the utilization of several function extraction stages that can automatically acquire representations from data. The availability of vast quantities of data, alongwith technology breakthroughs, has spurred CNN research, and numerous promising deep CNN designs have lately been released, as proposed in [PERSON] et al. (2020).  
This topic offers a lot of study potential because it is growing more popular and has less work. In a real-world context, it also has a wide range of applications. In a drone surveillance scenario, this may be useful. Pedestrian attribute recognition tasks have several applications, including public safety, soft bio-metrics, suspicious person identification, criminal investigation, and so on ([PERSON] et al., 2019).  
Our primary goal in this research is to recognize pedestrians in a multitude of pedestrian scenarios. We have used the Richly Annotated Pedestrian(RAP) v2 dataset to experiment. Mask RCNN is used to extract isolated pedestrians from a multiple pedestrian scenario. After that, we experimented with multiple pre-trained CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 152 v2, ResNet 101 v2, to obtain a better performing architecture. In addition, an extensive experiment on the proposed CNN architecture ResNet 152 v2 is performed by fine-tuning (freezing some layers). Many previous works ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020) applied transfer learning techniques for the pedestrian attribute recognition task. Transfer learning techniques displayed better performance in these works. In all cases, we used the transfer learning technique to obtain better results. However, the main contributions of the proposed framework are as follows:  
* Proposing a framework for recognizing attributes from a multiple pedestrian scenario.
* Applying the transfer learning technique among various CNN architectures, i.e., Inception ResNet v2, Xception, ResNet 152 v2, ResNet 101 v2 to recognize pedestrian attributes.
* Tuning the best performing model ResNet 152 v2 by freezing some layers and designing a customized fully connected layer.
* Analyzing the RAP v2 dataset and applying data balancing techniques, i.e., oversampling.