In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone.

4D facial generation aims to generate time-series 3D facial models, which has broad applications in digital entertainment, virtual reality, the metaverse, etc. However, this task remains particularly challenging due to the complex high-dimensional irregular mesh structures and the necessity to maintain both temporal smoothness and identity consistency. To address these challenges, we propose 4DM-LDDM, a mesh latent diffusion deformable model that innovatively integrates optimal transport theory and cascaded diffusion deformation models. Firstly, we employ a LSA-Conv-based mesh autoencoder with Wasserstein distance constraint to encode 3D facial meshes into a latent space. This framework ensures effective preservation of facial features while avoiding the high-dimensional complexity of directly generating 4D facial sequences based on the original 3D mesh. Subsequently, a diffusion deformation model in the latent space is proposed, which cascades two diffusion modules and one deformation module. The model learns spatial deformation information by estimating target latent codes and generating deformation fields via a spatial transformation layer, enabling continuous transitions from source to target meshes. This framework supports both unconditional and label-guided 4D facial generation. Experimental results demonstrate that our proposed 4DM-LDDM achieves smooth frame-to-frame transitions and high-quality facial animation sequences.