# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 2 Related Work  
The pedestrian attribute recognition task is a series of steps. In a surveillance scenario, an image might contain pedestrians. The pedestrian attribute recognition task addresses the recognition of attributes of each of the pedestrians. The first step of the pedestrian attribute recognition task is to detect the pedestrians as accurately as possible. Several traditional and deep learning methods are applied to detect pedestrians from a surveillance scenario. After that, the next step is to recognize the pedestrian attributes. Many robust CNN architectures are used for this task. Figure 1: Example of the pedestrian attributes from a multiple pedestrian scenario. Many traditional methods are applied to extract the pedestrians from a surveillance image. Face feature-based traditional methods are used to detect the face of a pedestrian. The use of colour space using YCbCr, HSV, etc., has a significant impact on detecting the face of multiple pedestrians [7]. Additionally, the normalization method in one of the colour space channels extended the detection accuracy. However, motion feature-based techniques are also applied for pedestrian detection. Region segmentation and machine learning-based classification technique is proposed for the pedestrian detection task [8]. Region segmentation is based on optical flow and feature extraction and classification based on Bandelet transform. Several component-based techniques are established to detect pedestrians. The main objective is to use body parts, i.e., head, arms, and leg., in an appropriate geometric configuration to detect the full pedestrian body. An AdaBoost learning algorithm is used to boost the accuracy of the Support Vector Machine(SVM) classifier for the component-based pedestrian detection task [9]. Recently, deep learning techniques have become very popular for object classification, object recognition, object detection tasks. The advancement of GPU, as well as image data, has made deep learning more efficient. Deep learning-based systems may recognize a single object or multiple objects in an image more accurately than traditional object detection algorithms. Many advanced deep learning methods are applied for the pedestrian detection task. A Convolutional Neural Network is proposed to detect the pedestrians from a surveillance scenario [10]. Three deep neural networks, i.e., supervised CNN, transfer learning-based CNN, and hierarchical extreme learning machine, are used for the task. The obtained results are very satisfactory. Additionally, many double stage CNN architectures are developed for the object detection task. These frameworks are also used to detect pedestrians. R-CNN [11], Fast R-CNN [12], Faster R-CNN [13], and Mask R-CNN [14] are the double stage CNN framework capable of detecting objects very effectively. The overall challenge for these frameworks is that they are typically slow for the large computation purpose of the two stages. This challenge is overcome by the single-stage CNN framework SSD [15], YOLO [16] as they contain only one stage which is responsible for the object detection task. However, the only challenge for single-stage detectors is detection accuracy. The Mask R-CNN can detect better in the case of object detection [17]. Thus, to better detect the pedestrians for the pedestrian attribute recognition task, we introduced Mask R-CNN in our proposed framework. The multi-label classification issue of pedestrian attribute recognition has been widely used to retrieve and re-identify individuals. Attribute analysis is becoming more common to infer high-level semantic details. Previously conducted research used conventional methods for resolving this issue. The AdaBoost algorithm can be used to make a discriminative recognition model [18]. This algorithm resolves the problem of creating a particular handcrafted feature. Another approach is the Ensemble RankSVM method [19] that solves the scalability problem caused by current SVM-based ranking methods for the pedestrian attribute recognition task.