# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 2 Related Work
An attribute aware pooling algorithm is proposed [30] that explores and exploits the association between attributes for the pedestrian attribute recognition challenge, extending the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition issue. Mutual learning of three attention mechanisms, i.e., Parsing attention, Label attention, and Spatial attention, is proposed [31] for pedestrian attribute analysis to select relevant and discriminative regions or pixels against variations. Additionally, an attention-based neural network made up of a Convolutional Neural Network, Channel Attention (CAtt), and Convolutional Long Short-Term Memory (ConvLSTM) (CNN-CAtt) is also suggested [32] for the pedestrian attribute recognition task. They intended to address the issue of pedestrian attribute recognition methods already in use. The reason for the poor performance is that they ignored the relationship between pedestrian attributes and spatial information. A novel Co-Attentive Sharing (CAS) module is proposed [33] to address the same problem, which extracts discriminative channels and partial regions for more efficient feature sharing in multi-task learning. An image-attribute reciprocal guidance representation (RGR) method is proposed [34] to explore the image-guided and attribute-guided features. They considered concrete attributes, i.e., hairstyle, shoe style, and abstract attributes, i.e., age range, role types. Moreover, a fusion attention method is used to allocate different attention levels to certain RGR aspects. Furthermore, a combination of focal and cross-entropy loss is used to solve the problem of attribute imbalance. A novel weighted cross-entropy loss function-based work has been introduced to reduce the class imbalance problem for the pedestrian attribute recognition task [35]. GoogleNet is used as the base CNN architecture, and features from different layers of the architecture are passed into a Flexible Spatial Pyramid Pooling layer (FSPP). The outputs of these FSPP are passed to a neural network for classification purposes. Additionally, an adaptively weighted deep framework is proposed [36] to learn the multiple person attributes. Moreover, validation loss is used to automatically update the weights of the CNN architecture, i.e., ResNet 50. Their work's main motive is to prioritize the important task by adding higher weights on those tasks. Similar approaches, i.e., clothing attribute prediction, related to pedestrian attribute recognition, can help in the person re-identification task. A task-aware attention mechanism is proposed [37] for the clothing attribute prediction task to explore the impact of each position across different tasks. A cloth detector is used to explore the target region, and a CNN architecture extracts the features of that region. The task and spatial attention modules are used to learn the feature maps. For optimization, the t-distribution Stochastic Triplet Embedding loss function is used. As a result, pedestrian attribute recognition has been significant for years. Additionally, deep learning models combined with a broad range of tuning and applying transfer learning techniques have recently surpassed established state-of-the-art methods. In our proposed framework, we used transfer learning techniques and tuning of the hyperparameters to obtain satisfactory results. Additionally, implementing this research in a surveillance situation is a novel notion that helps to increase people's security.