Wildfire monitoring systems play a critical role in minimizing environmental and societal damage. Recent advances in computer vision, particularly deep learning-based fire detection, have enabled more accurate and scalable solutions. However, conventional fire detection methods often struggle with wildfire scenarios due to wide spatial extent, the demand for precise localization, and the urgency of early response. To overcome these challenges, we propose a wildfire monitoring framework capable of pixel-level fire origin localization mapped onto a GPS-calibrated digital twin of mountainous terrain. Our system integrates visual fire detection with terrain-aware 3D projection, enabling accurate mapping of fire origins to real-world coordinates. Experimental results on wildfire datasets demonstrate that our method achieves high accuracy in both early fire detection and precise localization, offering a practical and scalable solution for real-world wildfire monitoring.

Retrieving the target vehicle through natural language descriptions plays a crucial role in intelligent transportation systems. Existing methods tackle this task by employing models that leverage the correlation between textual and visual representations, such as CLIP. However, these models struggle to capture the temporal characteristics of video data, and researchers enhance temporal understanding performance through various data augmentation and video encoders. Yet, conventional approaches in previous studies often overlook the detailed temporal characteristics of vehicles. To overcome this limitation, we introduce a MOVES: Motion-Oriented VidEo Sampling method to effectively utilize the motion information of the target vehicle. Furthermore, we construct a robust model by implementing a re-ranking algorithm to address a variety of vehicle attributes. As a result, our proposed model achieves state-of-the-art performance on the public vehicle retrieval dataset.

Person Re-IDentification (ReID) is a pivotal method for pedestrian tracking and retrieval. This research is inherently challenged by large changes in intra-class or small changes in inter-class. To address this challenge, many researchers have recently introduced transformer-based models, which have shown excellent results. The primary objective of these models is to generate robust features that effectively distinguish between classes and enable generalization. However, existing methods still suffer from class discrimination due to unnecessary noise, including the background. To overcome this limitation, we propose a novel approach called Two stream-based Regionally Enhanced Transformers (TRET) that focuses on the target to be identified. To concentrate on the target region, the TRET utilizes a structure that leverages the pedestrian mask. Furthermore, the proposed model generalizes well by utilizing Contrastive Language-Image Pretraining as the backbone. Finally, our proposed model achieves state-of-the-art performance on the public datasets.

Person re-identification (ReID) plays a significant role in intelligent surveillance systems. However, it is challenging due to large variations in the intra-class, where the same person is captured in different scenes or cameras. The current person ReID research focuses on creating robust features for class distinction and generalizing neural networks for covering various target domains to address the issue. Recently, after the achievement of vision transformers, the application of transformers has also begun to person ReID studies. The transformer-based methods have improved quantitative performance of person ReID; however, they still suffer from class distinction. Therefore, this paper proposes a novel region-enhanced transformer (REET) to create robust ReID features. Unlike conventional transformer-based approaches, the REET emphasizes the tokens generated by region-level. Our method achieves state-of-the-art results on three public datasets; Market1501, DukeMTMC, and CUHK-03.

This paper proposes a colorimetric characterization method using the color correlation between the colorants in a hi-fi printer. While several colorant combinations can be used to match a certain color stimulus in a hi-fi printing system with more than 3 colorants, conventional colorimetric characterization methods only use 3 or 4 colorants to render a color, thereby limiting the color representation. As a result, the gamut is limited as they give up the other combinations of colorants. Therefore, this paper proposes a method of colorimetric characterization that uses combinations of all the colorants. As such, certain colorant combinations are selected based on considering the correlation factor between the colorant amount distributions. The correlation factor also affects the interpolation error, as the colorants are not independent of each other. Consequently, the total gamut is increased in low lightness regions, and the colors are represented more accurately.