As a periodic movement, gait contains informative biometric traits formed by individual body structures, motion patterns, and behavioral habits. Previous gait recognition methods mainly focus on mining the appearance cues from gait sequences, while neglecting the dynamic motion characteristics. Motion cues are important complementary information for generating high-quality gait representations that can help models accurately recognize individuals. In this article, we propose a novel gait recognition framework named GaitDS to model dynamic motion information and construct salient gait representations. Specifically, we develop a motion information perception module that can directly represent dynamic regions during walking and extract fine-grained motion features based on the appearance of body parts over time. In addition, since some frames in gait sequences share partial similarities, we present saliency identity representation learning to focus on key frames along the temporal dimension, and integrate salient identity features to enhance sequence-level representations. Furthermore, a channel enhanced module is designed to generate more discriminative gait representations, where motion and temporal salient features can be complemented with global representations. Compared with existing state-of-the-art methods, our model achieves superior average rank-1 recognition accuracy on three benchmark datasets, i.e., 93.7% on CASIA-B, 92.4% on OU-MVLP, and 50.7% on Gait3D.

Crowd counting remains a challenging vision task due to the presence of several problems such as severe occlusions, perspective distortions and scale variations in the target scene. How to design an accurate and robust crowd counting estimator has attracted intensive research interest in the past few decades. It is well-known that learning rich features representation is crucial for crowd counting. However, the existing neural-networks-based methods only employ CNN features extracted from the last convolutional layer, and the useful hierarchical information contained in the CNN features is overlooked. To address this problem, we propose a CNN architecture based on the fully convolutional network, which is used to build an end-to-end density map estimation system by combining some of the meaningful convolutional features. Such a combination is exploited to effectively capture both the multi-scale and the multi-level information in complex scenes. Extensive experiments on most existing crowd counting dataset- s including ShanghaiTech Part A, ShanghaiTech Part B and UCF CC 50 demonstrate the effectiveness and the reliability of our approach.