With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.

We introduce the KAIST multi-spectral data set, which covers a great range of drivable regions, from urban to residential, for autonomous systems. Our data set provides the different perspectives of the world captured in coarse time slots (day and night), in addition to fine time slots (sunrise, morning, afternoon, sunset, night, and dawn). For all-day perception of autonomous systems, we propose the use of a different spectral sensor, i.e., a thermal imaging camera. Toward this goal, we develop a multi-sensor platform, which supports the use of a co-aligned RGB/Thermal camera, RGB stereo, 3-D LiDAR, and inertial sensors (GPS/IMU) and a related calibration technique. We design a wide range of visual perception tasks including the object detection, drivable region detection, localization, image enhancement, depth estimation, and colorization using a single/multi-spectral approach. In this paper, we provide a description of our benchmark with the recording platform, data format, development toolkits, and lessons about the progress of capturing data sets.

Multispectral pedestrian detection has been actively studied as a promising multi-modality solution to handle illumination and weather changes. Most multi-modality approaches carry the assumption that all inputs are fully-overlapped. However, these kinds of data pairs are not common in practical applications due to the complexity of the existing sensor configuration. In this letter, we tackle multispectral pedestrian detection, where all input data are not paired. To this end, we propose a novel single-stage detection framework that leverages multi-label learning to learn input state-aware features by assigning a separate label according to the given state of the input image pair. We also present a novel augmentation strategy by applying geometric transformations to synthesize the unpaired multispectral images. In extensive experiments, we demonstrate the efficacy of the proposed method under various real-world conditions, such as fully-overlapped images and partially-overlapped images, in stereo-vision. Code and a demonstration video are available at https://github.com/sejong-rcv/MLPD-Multi-Label-Pedestrian-Detection.

With the advent of commodity autonomous mobiles, it is becoming increasingly prevalent to recognize under extreme conditions such as night, erratic illumination conditions. This need has caused the approaches using multi-modal sensors, which could be complementary to each other. The choice for the thermal camera provides a rich source of temperature information, less affected by changing illumination or background clutters. However, existing thermal cameras have a relatively smaller resolution than RGB cameras that has trouble for fully utilizing the information in recognition tasks. To mitigate this, we aim to enhance the low-resolution thermal image according to the extensive analysis of existing approaches. To this end, we introduce Thermal Image Enhancement using Convolutional Neural Network (CNN), called in TEN, which directly learns an end-to-end mapping a single low resolution image to the desired high resolution image. In addition, we examine various image domains to find the best representative of the thermal enhancement. Overall, we propose the first thermal image enhancement method based on CNN guided on RGB data. We provide extensive experiments designed to evaluate the quality of image and the performance of several object recognition tasks such as pedestrian detection, visual odometry, and image registration.

Recently, transformers have been widely adopted for various computer vision tasks and show promising results due to their ability to encode long-range spatial dependencies in an image effectively. However, very few studies on adopting transformers in self-supervised depth estimation have been conducted. When replacing the CNN architecture with the transformer in self-supervised learning of depth, we encounter several problems such as problematic multi-scale photometric loss function when used with transformers and, insufficient ability to capture local details. In this letter, we propose an attention-based decoder module, Pixel-Wise Skip Attention (PWSA), to enhance fine details in feature maps while keeping global context from transformers. In addition, we propose utilizing self-distillation loss with single-scale photometric loss to alleviate the instability of transformer training by using correct training signals. We demonstrate that the proposed model performs accurate predictions on large objects and thin structures that require global context and local details. Our model achieves state-of-the-art performance among the self-supervised monocular depth estimation methods on KITTI and DDAD benchmarks.

Nitrite plays a major role in inhibiting the growth of foodborne pathogens, including Clostridium botulinum (C. botulinum) that causes botulism, a life-threatening disease. Nitrite serves as a color-fixing agent in processed meat products. However, N-nitroso compounds can be produced from nitrite, which are considered as carcinogens. Thus, consumers desire processed meat products that contain lower concentrations (below conventional concentrations of products) of nitrite or no nitrite at all, although the portion of nitrite intake by processed meat consumption in total nitrite intake is very low. However, lower nitrite levels might expose consumers to risk of botulism poisoning due to C. botulinum or illness caused by other foodborne pathogens. Hence, lower nitrite concentrations in combination with other factors such as low pH, high sodium chloride level, and others have been recommended to decrease the risk of food poisoning. In addition, natural compounds that can inhibit bacterial growth and function as color-fixing agents have been developed to replace nitrite in processed meat products. However, their antibotulinal effects have not been fully clarified. Therefore, to have processed meat products with lower nitrite concentrations, low pH, high sodium chloride concentration, and others should also be applied together. Before using natural compounds as replacement of nitrite, their antibotulinal activities should be examined.