Human posture equipment technology has advanced significantly thanks to advances in deep learning and machine vision. Even the most advanced models may not be able to predict all body joints accurately. This paper proposes an adaptive generative adversarial network to improve the human posture detection algorithm in order to address this issue. GAN is used in the algorithm to detect human posture improvement. The algorithm uses OpenPose to detect and connect keypoints and then generates heat maps in the GAN system model. During the training process, the confidence evaluation mechanism is added to the system model. The generator predicts posture, while the resolver refines human joints over time. And, by using normalization technologies in the confidence evaluation mechanism, the generator can pay more attention to the prominent body joints, improving the algorithm’s body detection accuracy of nodes. In MPII, LSP, and FLIC datasets, the proposed algorithm has shown to have a good detection effect. Its positioning accuracy is about 95.37 percent, and it can accurately locate the joints of the entire body. Several other algorithms are outperformed by this one. The algorithm described in this article has the best simultaneous runtime in the LSP dataset.

The current dialog model suffers from the lack of grammatical accuracy and content relevance. To provide interactive content with high quality, the study constructs an intelligent human-computer interaction dialog model using end to end neural network and emotion information. The study utilizes the transformer-based bidirectional coding model and the end to end neural network model to realize the interactive dialogue, and embeds emotional information in the dialogue model to realize the intelligent human-computer interaction dialogue. The outcomes demonstrated that the classification accuracy of the research design sentiment classification model on the three datasets was 89.97%, 90.15% and 91.44%, respectively. The research-designed model consistently outperformed the other models in terms of emotion accuracy as well as Distinct-1 and Distinct-2 score values, which were 0.068, 0.204, and 80.425%, respectively. Meanwhile, the average accuracy of the research-designed model was higher than 90% on all three datasets. The emotional quality and content quality of the model during the dialog process are higher than several more popular emotional interaction dialog models. The comprehensive analysis outcomes reveal that the research-designed model is able to generating high-quality emotion-based responses in the conversation, providing users with a more intelligent human-computer interaction experience.

In response to the problems of target loss and insufficient accuracy in existing target perception and action recognition algorithms, this paper proposes a target perception and action recognition algorithm based on saliency and feature extraction. This algorithm uses saliency detection techniques to obtain salient regions in images or videos to focus attention on the target. At the same time, feature extraction techniques are combined to extract key nodes and inter-frame correlations from the target information. The experimental results of the measurement data show that this algorithm is superior to traditional detection methods in detecting target behaviour. In addition, it has successfully solved the problems of motion misalignment and jumping in pedestrian detection. Although the node localisation of the algorithm needs further improvement, it has shown good application prospects in smart cities and intelligent surveillance. Future work will focus on improving the positioning accuracy of key nodes to enhance its adaptability to different environments and scenarios, providing better support for smart city and other applications.

Target tracking is prone to problems such as target loss and identity jump when the target is occluded and the attitude is changed. In order to solve this phenomenon, this paper proposes the adaptive adjustment object detection algorithm under multiple mechanisms based on GAN. This algorithm introduces a gradient penalty mechanism to the discriminator and uses the relative discriminator structure to reconstruct the discriminator, so as to improve the discriminatory ability of the discriminator. Then, through the feedback mechanism, the obtained data is fed back to the generator network in time, and the genetic mechanism is used to speed up the positioning of the key areas of the image. Experimental results show that compared with other existing algorithms, this algorithm can effectively locate and distinguish under different environments. And, the target still maintains a high resolution. When the target is occluded, it can effectively avoid the phenomenon of target loss and identity jump.

Within the realm of computer vision, the task of re-identifying targets across multiple video frames has emerged as a pivotal challenge, particularly in domains like video surveillance, smart transportation systems, and pedestrian flow analytics. Conventional re-identification techniques often grapple with constraints stemming from varying camera perspectives, inconsistent lighting conditions, and prevalent occlusions. Addressing these challenges, this research introduces MVF-Re, a sophisticated re-identification approach that synergizes adaptive attention mechanisms with multi-scale feature fusion. Initially, we architect a deep attention-enhanced feature pyramid network, a pioneering framework that dynamically tailors itself to video frame content, thereby capturing intricate target details. Subsequently, we incorporate a multi-input Siamese network, ensuring the derivation of consistent and resilient feature sets across diverse contexts. To augment feature distinctiveness, we conceptualize a context-sensitive dynamic attention mechanism, adept at judiciously allocating weights to individual video frames. Culminating our approach, we deploy an innovative multi-scale feature fusion methodology, offering a holistic and robust target representation. Empirical evaluations on multiple benchmark datasets underscore the superior performance of our methodology, underscoring its proficiency in multi-frame target re-identification.

With the rapid progress of computer vision and deep learning techniques, accurately predicting continuous human motions from very few image inputs and generating high-quality 3D human models has become a cutting-edge research direction in this field. Despite the achievements in 2D to 3D conversion techniques, it is still a great challenge to capture coherent movements from limited image frames and generate texture-rich 3D models. In this paper, we propose a 3D clothed human body generation method based on Inter-Frame Motion Prediction (IFMP for short) of 2D images, which is capable of not only predicting a series of coherent human body motions, but also reconstructing a detailed textured 3D human body model from only two image frames. The method automatically focuses on key parts of the image through action coding and uses a conditional generative adversarial network to generate a series of consecutive intermediate frame images. A depth-aware implicit function representation is combined to map a 3D model from the 2D image, and high quality textures of the human body in a clothed state are obtained by texture mapping and model detail enhancement methods. Finally, the experimental results validate the advantages of the IFMP method in image action coherence prediction, as well as verifying the effectiveness of the human 3D model generated by the method in terms of geometric accuracy and texture quality.