Drone-view object detection has shown noticeable performances and has been adopted by various real-world applications. However, there exist still several problems to be handled for its safe usage. While most existing methods have tried to manage a variety of object scales, there are very few works to deal with diverse weather conditions. Therefore, in this paper, we propose a novel approach to build a drone-view object detector robust against the adverse effects of diverse environmental factors, such as foggy, rainy, and low illumination. To this end, we generated a weather content feature set using a multimodal large language model (MLLM), to describe diverse weather, illumination, and visibility conditions. These features are then adaptively selected based on the input image and applied to the detection framework to recognize the environmental semantics in the given visual images. Hereby, a detection framework can have environmental context understanding capability in drone-view images. With the comprehensive experiments and analysis, we corroborate the effectiveness of the proposed method showing the robustness against adverse weather conditions.

Pedestrian detection is a crucial field of computer vision research which can be adopted in various real-world applications ( e.g., self-driving systems). However, despite noticeable evolution of pedestrian detection, pedestrian representations learned within a detection framework are usually limited to particular scene data in which they were trained. Therefore, in this paper, we propose a novel approach to construct versatile pedestrian knowledge bank containing representative pedestrian knowledge which can be applicable to various detection frameworks and adopted in diverse scenes. We extract generalized pedestrian knowledge from a large-scale pretrained model, and we curate them by quantizing most representative features and guiding them to be distinguishable from background scenes. Finally, we construct versatile pedestrian knowledge bank which is composed of such representations, and then we leverage it to complement and enhance pedestrian features within a pedestrian detection framework. Through comprehensive experiments, we validate the effectiveness of our method, demonstrating its versatility and outperforming state-of-the-art detection performances.

Large language models (LLMs) have shown their capabilities in understanding contextual and semantic information regarding knowledge of instance appearances. In this paper, we introduce a novel approach to utilize the strengths of LLMs in understanding contextual appearance variations and to leverage this knowledge into a vision model (here, pedestrian detection). While pedestrian detection is considered one of the crucial tasks directly related to our safety (e.g., intelligent driving systems), it is challenging because of varying appearances and poses in diverse scenes. Therefore, we propose to formulate language-derived appearance elements and incorporate them with visual cues in pedestrian detection. To this end, we establish a description corpus that includes numerous narratives describing various appearances of pedestrians and other instances. By feeding them through an LLM, we extract appearance knowledge sets that contain the representations of appearance variations. Subsequently, we perform a task-prompting process to obtain appearance elements which are guided representative appearance knowledge relevant to a downstream pedestrian detection task. The obtained knowledge elements are adaptable to various detection frameworks, so that we can provide plentiful appearance information by integrating the language-derived appearance elements with visual cues within a detector. Through comprehensive experiments with various pedestrian detectors, we verify the adaptability and effectiveness of our method showing noticeable performance gains and achieving state-of-the-art detection performance on two public pedestrian detection benchmarks (i.e.,CrowdHumanandWiderPedestrian).

Recently, although multispectral pedestrian detection has achieved remarkable performances, there is still a problem to be handled, position shift problem. Due to the problem, a pedestrian looks like existing in different positions between each modal image. Then, a single bounding box usually fails to capture an entire pedestrian properly in both modal images at the same time, which means it would not contain some parts of a pedestrian and includes noisy backgrounds instead. In this paper, we propose a novel approach, that is, a pedestrian feature mapping from mis-captured pedestrian features to well-captured pedestrian features which encode an entire pedestrian properly in both modal images. To this end, we utilize a memory architecture which stores well-captured pedestrian features, and then, the well-captured features can enhance the quality of pedestrian representation by providing the distinctive information of a pedestrian. We validate the effectiveness of our approach with comprehensive experiments on two multispectral pedestrian detection datasets, achieving state-of-the-art performances.

Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot cope with other modal inputs. In addition, recent multispectral pedestrian detectors have shown remarkable performance by adopting multispectral modalities, but they also have limitations in practical applications (e.g., different Field-of-View (FoV) and frame rate). In this paper, we introduce a versatile pedestrian detector that shows robust detection performance in any single modality. We propose a multisensory-matching contrastive loss to reduce the difference between the visual representation of pedestrians in the visible and thermal modalities. Moreover, for the robust detection on a single modality, we design a Multispectral Recalling (MSR) Memory. The MSR Memory enhances the visual representation of the single modal features by recalling that of the multispectral modalities. To guide the MSR Memory to store the multispectral modal contexts, we introduce a multispectral recalling loss. It enables the pedestrian detector to encode more discriminative features with a single input modality. We believe our method is a step forward detector that can be applied to a variety of real-world applications. The comprehensive experimental results verify the effectiveness of the proposed method.

Recently, pedestrian detection on thermal infrared images has shown the robust pedestrian detection performance. In this paper, we propose a novel thermal infrared pedestrian detection framework which can associate and utilize the complementary pedestrian knowledge from visible images. Motivated by that humans can associate useful information from other sensors to perform a more reliable decision, we devise a Visible-sensory Pedestrian Associating (VPA) Memory to conduct the robust pedestrian detection by utilizing complementary visible-sensory pedestrian knowledge explicitly. The VPA Memory is trained to store the pedestrian information of visible images and associate it with a given thermal infrared pedestrian knowledge via the memory associating learning. We verify the effectiveness of the proposed framework with extensive experiments, and it achieves state-of-the-art pedestrian detection performance on thermal infrared images.

Multispectral pedestrian detection has received great attention in recent years as multispectral modalities (i.e. color and thermal) can provide complementary visual information. However, there are major inherent issues in multispectral pedestrian detection. First, the cameras of the two modalities have different field-of-views (FoVs), so that image pairs are often miscalibrated. Second, modality discrepancy is observed, because image pairs are captured at different wavelengths. In this paper, to alleviate these issues, we propose a new uncertainty-aware multispectral pedestrian detection framework. In our framework, we consider two types of uncertainties: 1) Region of Interest (RoI) uncertainty and 2) predictive uncertainty. For the miscalibration issue, we propose RoI uncertainty which represents the reliability of the RoI candidates. With the RoI uncertainty, when combining two modal features, we devise uncertainty-aware feature fusion (UFF) module to reduce the effect of RoI features with high RoI uncertainty. We also propose uncertainty-aware cross-modal guiding (UCG) module for the modality discrepancy. In the UCG module, we use the predictive uncertainty, which indicates how reliable the prediction of the RoI feature is. Based on the predictive uncertainty, the UCG module guides the feature distribution of high predictive uncertain (less reliable) modality to resemble that of low predictive uncertain (more reliable) modality. The UCG module can encode more discriminative features by guiding feature distributions of two modalities to be similar. With comprehensive experiments on the public multispectral datasets, we verified that our method reduces the effect of the miscalibration and alleviates the modality discrepancy, outperforming existing state-of-the-art methods.

Although the visual appearances of small-scale objects are not well observed, humans can recognize them by associating the visual cues of small objects from their memorized appearance. It is called cued recall. In this paper, motivated by the memory process of humans, we introduce a novel pedestrian detection framework that imitates cued recall in detecting small-scale pedestrians. We propose a large-scale embedding learning with the large-scale pedestrian recalling memory (LPR Memory). The purpose of the proposed large-scale embedding learning is to memorize and recall the large-scale pedestrian appearance via the LPR Memory. To this end, we employ the large-scale pedestrian exemplar set, so that, the LPR Memory can recall the information of the large-scale pedestrians from the small-scale pedestrians. Comprehensive quantitative and qualitative experimental results validate the effectiveness of the proposed framework with the LPR Memory.

The performance of recent deep neural networks in various computer vision areas such as object detection has increased significantly. Along with such advances, attempts to visualize and interpret the networks have been made in order to understand how a network predicts a certain result. However, there is a lack of research on ways to improve the interpretability of networks’ features. In this paper, we propose a spatial relation reasoning (SRR) framework to encode interpretable networks’ features, especially an object detector, by mimicking the human visual cognition system. The SRR consists of the spatial feature encoder (SFE) and the graph-based spatial relation encoder (GSRE) to consider spatial relationships between different parts of an object. So that, object detectors can encode spatially-related object features enabling humanlike visual interpretation. We verified the proposed framework with general object detectors on public datasets-PAS-CAL VOC and MS COCO.