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Gait recognition is an important biometric technology that allows for the remote collection of stakeholders' characteristics, without requiring their explicit cooperation. It has gained considerable attention in the fields of criminal investigation and intelligent security. Previous studies have shown that local gait features can enhance gait recognition performance by improving robustness to disturbances. However, global gait features also play a crucial role in gait recognition. Many researchers have utilized convolutional operations to extract global features, but these operations tend to focus on features within the receptive field, neglecting those outside of it. Therefore, the potential of global gait features has not been fully explored. In this paper, we propose a gait recognition framework based on vision transformers, aiming to enhance the extraction of global gait features. We introduce an adaptive multi-frame global feature mapping (AMGM) method to address the challenge of inconsistent feature dimensions caused by variations in the number of gait frames when fusing global and local features. We evaluate our model on the latest datasets, and the experimental results demonstrate a significant breakthrough. Notably, our model achieves state-of-the-art recognition accuracy, particularly in scenarios where subjects are wearing coats. Additionally, our model achieves remarkable improvements in recognition accuracy through training with small sample sets. [ABSTRACT FROM AUTHOR]

The images in foggy weather are often degraded which may affect the object detection process. Hence, in recent times, several schemes have been designed to eliminate the haze effect and enhance the performance of computer vision systems. Although these methods reduce the haze effect, they also produce over-saturation and over-degradation on most occasions. These problems occur due to inadequate utilization of the regional haze properties during the dehazing process. Therefore, a new dehazing model is proposed in this paper to address the issues faced by existing dehazing algorithms. The residual convolutional neural network (RCNN) with 14 layers is developed in the present work. In this model, features to indicate the extent of haze are extracted using the regional characteristics of a hazy image and are used as the fourth channel along with the three colour channels of a hazy image. The proposed RCNN is trained with 4-channel hazy images as input and their corresponding haze-free images as output. The results of our model are obtained with several images taken from three different datasets. Through the experimentation, quantitative and qualitative improvements are observed when compared to state-of-the-art dehazing methods. • The local-haze characteristics driven dehazing method is developed in this work. • The regional haze properties are extracted using pixel-wise local max-min difference and pixel intensities of the hazy image. • A 4-channel hazy image is developed by concatenating the regional haze properties map to the hazy image. • A 14-layered RCNN is developed with the 4-channel hazy image as input and the haze-free image as output. [ABSTRACT FROM AUTHOR]