Attention Based CNNConvLSTM for Pedestrian Attribute Recognition As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multilabel image classification problem. An attentionbased neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long shortterm memory (ConvLSTM) is proposed (CNNCAttConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pretrained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other stateoftheart (SOTA) methods is achieved, which proves the superiority and validity of our method.

Joint Learning of Dictionary and Convolutional Network for Pedestrian Attribute Recognition Pedestrian attribute recognition is to predict the presence of a set of attributes from a given image, and it plays an important role in video surveillance applications. Most existing works model the task as a multilabel classification problem. Although effective, they ignore the existence of correlations among attributes. In this work, to learn multiple attributes jointly, the attributes are modeled as a subspace and a dictionary is introduced to represent the subspace. Furthermore, to extract the convolutional features which are more suitable for attribute prediction, the dictionary is modeled as a network layer which is learned jointly with the convolutional network. Finally, a novel learning algorithm is proposed to optimize the dictionary and the convolutional network corporately. Extensive experimental analyses and evaluations on two largest pedestrian attribute benchmarks PETA and PA100K demonstrate that the proposed method achieves stateoftheart performance.