Objective: To develop a robust and effective computer vision system that can automatically identify and classify human actions in video data, considering the temporal dynamics and various environmental conditions. This technology has numerous applications in surveillance, human-computer interaction, and video analysis. Methods: The key methods for dense trajectory extraction include the dense optical flow, which computes motion vectors for each point, and the use of key point detectors like the Scale-Invariant Feature Transform (SIFT) or the Harris corner detector. Findings: By describing the motion of the trajectories, trajectory descriptors produce remarkably strong results on their own, such as 90.2% on KTH and 47.7% on Hollywood2 for dense trajectories. This demonstrates the significance of the motion data present in the local trajectory patterns. Because the trajectory descriptors catch a lot of camera motion, we only report 67.2% on YouTube. Novelty: In this study, a method for modelling movies that combines dense sampling and feature tracking is presented. Compared to earlier video descriptions, our dense trajectories are more reliable. They effectively capture the motion data in the movies and outperform cutting-edge action categorization techniques in terms of performance. Keywords: Human action recognition, Scale-Invariant feature transform, Histograms of oriented gradients, Spatial and temporal interest points, Histograms of optical flow

Precision and efficacy are vital in the constantly advancing field of food image identification, particularly in the domains of medicine and healthcare. Transfer learning and deep ensemble learning techniques are employed to enhance the accuracy and efficiency of the Indian Food Classification System. The ensemble model effectively captures various patterns and correlations within the information by employing many machine learning techniques. The ensemble method we employ utilizes the MobileNetV3 and DenseNet-121 transfer learning models to construct a robust model. The ensemble model benefits from the integration of model predictions, resulting in enhanced recognition of Indian food. The study utilized a dataset consisting of 6000 photographs of Indian cuisine, categorized into 26 distinct groups. The picture dataset is divided into two subsets: 80% is allocated for training and 20% is reserved for testing. The experimental results demonstrate that DenseNet-121 surpasses MobileNetv3 in terms of testing accuracy, achieving a rate of 90%. The MobileNetV3 model achieves an accuracy of 87.64% on the Indian food image dataset. The integration of both models in ensemble learning yields a model accuracy of 92.38%, surpassing the performance of each individual model. This research revolutionizes our food relationship with the use of state-of-the-art technologies. By utilizing the most advanced transfer learning algorithm specifically designed for the precise classification of Indian cuisine, our aim is to establish a new standard in both technology and gastronomy. This will facilitate innovation in food perception, comprehension, and engagement.

This study examines the utilization of deep learning and transfer learning models for classifying photos of Indian cuisine. Indian cuisine, characterized by its extensive diversity and intricate presentation, poses considerable hurdles in food recognition owing to changes in ingredients, texture, and visual aesthetics. To tackle these challenges, we utilized a bespoke Convolutional Neural Network (CNN) and harnessed cutting-edge transfer learning models such as DenseNet121, InceptionV3, MobileNet, VGG16, and Xception. The research employed a varied dataset comprising 13 food categories and executed preprocessing techniques like HSV conversion, noise reduction, and edge identification to improve image quality. Metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were employed to assess model efficacy. The CNN model demonstrated a mediocre performance, revealing overfitting concerns due to a substantial disparity between training and validation accuracy. In contrast, transfer learning models, particularly DenseNet121, InceptionV3, and Xception, exhibited an enhanced generalization ability, each attaining above 92% accuracy across all criteria. MobileNet and VGG16 produced reliable outcomes with marginally reduced performances. The results highlight the efficacy of transfer learning in food image classification and indicate that fine-tuned, pre-trained models markedly improve classification accuracy. This research advances the creation of intelligent food recognition systems applicable in dietary monitoring, nutrition tracking, and health management.