Pedestrian detection is a crucial task in computer vision. Utilizing multispectral knowledge, especially, is essential to effectively detect the pedestrians. Existing multispectral pedestrian detection methods, however, perform only in fully-supervised situations. Although studies on semi-supervised object detection have been conducted, they focus only on single modality environments. Therefore, we propose novel semi-supervised multispectral pedestrian detector (SSMPD) that effectively utilizes multispectral knowledge. Our SSMPD consists of three methods that effectively address the pseudo-labels in the multispectral domain and a novel data selection method. First, we introduce a Pedestrian Appearance-Aware (PAA) weight to consider the quality of the pseudo-label by adjusting the multispectral knowledge transfer from the teacher model to the student model. Second, we propose a Unified Modal-Aware Simultaneous (UMAS) learning to consider the single modality (visible or thermal) and multispectral modalities when learning with the pseudo-label. Finally, we introduce a Similarity-based Contrastive (SC) loss to guide the teacher model in enhancing the quality of pseudo-labels. In addition, we provide diverse data selection for more effective semi-supervised learning. Extensive experimental results on the KAIST and LLVIP datasets demonstrate the effectiveness of our method.

Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality of pseudo-labels for missing annotations, and (ii) they rely on fixed ground truth annotations, which leads to learning only a limited range of pedestrian visual appearances in the multispectral domain. To address these issues, we propose a novel framework called Sparsely Annotated Multispectral Pedestrian Detection (SAMPD). For limitation (i), we introduce Multispectral Pedestrian-aware Adaptive Weight (MPAW) and Positive Pseudo-label Enhancement (PPE) module. Utilizing multispectral knowledge, these modules ensure the generation of high-quality pseudo-labels and enable effective learning by increasing weights for high-quality pseudo-labels based on modality characteristics. To address limitation (ii), we propose an Adaptive Pedestrian Retrieval Augmentation (APRA) module, which adaptively incorporates pedestrian patches from ground-truth and dynamically integrates high-quality pseudo-labels with the ground-truth, facilitating a more diverse learning pool of pedestrians. Extensive experimental results demonstrate that our SAMPD significantly enhances performance in sparsely annotated environments within the multispectral domain.

The deblocking filter in H.264/AVC is used on low-end terminals to a limited extent due to computational complexity. In this paper, variable block-based deblocking filter that efficiently eliminates the blocking artifacts occurred during the compression of low-bit rates digital motion pictures is suggested. Blocking artifacts are plaid images appear on the block boundaries due to DCT and quantization. In the method proposed in this paper, the image's spatial correlational characteristics are extracted by using the variable block information of motion compensation; the filtering is divided into 4 modes according to the characteristics, and adaptive filtering is executed in the divided regions. The proposed deblocking method eliminates the blocking artifacts, prevents excessive blurring effects, and improves the performance about 40% compared with the existing method.